Parallel de-rate-matching and layer demapping for physical uplink shared channel

ABSTRACT

Apparatuses, systems, and techniques to cause information received from a plurality of fifth-generation (5G) new radio (NR) antennas to be decoded in parallel. In at least one embodiment, the information includes data that has been processed by soft demapping, and decoding the information includes layer demapping, descrambling, and de-rate-matching the data in parallel.

FIELD OF INVENTION

At least one embodiment pertains to processing resources used to processwireless communications information for decoding. For example, at leastone embodiment pertains to parallel processors or computing systems usedto de-rate-match and layer demap wireless communications informationaccording to various novel techniques described herein.

BACKGROUND

Processing wireless communications signals and data for decoding can usesignificant computing resources and time. Approaches to processingwireless communications signals and data can be improved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a fifth-generation (5G) signalprocessing environment, according to at least one embodiment;

FIG. 2 is a block diagram illustrating a 5G new radio (NR) physicaluplink shared channel (PUSCH) processing pipeline, according to at leastone embodiment;

FIG. 3 is a block diagram illustrating layer demapping andde-rate-matching, according to at least one embodiment;

FIG. 4 is a block diagram illustrating layer demapping andde-rate-matching with single read/write operations, according to atleast one embodiment;

FIG. 5 is a block diagram illustrating layer demapping andde-rate-matching with shared memory, according to at least oneembodiment;

FIG. 6 illustrates a flowchart of a technique of layer demapping andde-rate-matching, according to at least one embodiment;

FIG. 7 illustrates an example data center system, according to at leastone embodiment;

FIG. 8A illustrates an example of an autonomous vehicle, according to atleast one embodiment;

FIG. 8B illustrates an example of camera locations and fields of viewfor the autonomous vehicle of FIG. 8A, according to at least oneembodiment;

FIG. 8C is a block diagram illustrating an example system architecturefor the autonomous vehicle of FIG. 8A, according to at least oneembodiment;

FIG. 8D is a diagram illustrating a system for communication betweencloud-based server(s) and the autonomous vehicle of FIG. 8A, accordingto at least one embodiment;

FIG. 9 is a block diagram illustrating a computer system, according toat least one embodiment;

FIG. 10 is a block diagram illustrating computer system, according to atleast one embodiment;

FIG. 11 illustrates a computer system, according to at least oneembodiment;

FIG. 12 illustrates a computer system, according at least oneembodiment;

FIG. 13A illustrates a computer system, according to at least oneembodiment;

FIG. 13B illustrates a computer system, according to at least oneembodiment;

FIG. 13C illustrates a computer system, according to at least oneembodiment;

FIG. 13D illustrates a computer system, according to at least oneembodiment;

FIGS. 13E and 13F illustrate a shared programming model, according to atleast one embodiment;

FIG. 14 illustrates exemplary integrated circuits and associatedgraphics processors, according to at least one embodiment;

FIGS. 15A and 15B illustrate exemplary integrated circuits andassociated graphics processors, according to at least one embodiment;

FIGS. 16A and 16B illustrate additional exemplary graphics processorlogic according to at least one embodiment;

FIG. 17 illustrates a computer system, according to at least oneembodiment;

FIG. 18A illustrates a parallel processor, according to at least oneembodiment;

FIG. 18B illustrates a partition unit, according to at least oneembodiment;

FIG. 18C illustrates a processing cluster, according to at least oneembodiment;

FIG. 18D illustrates a graphics multiprocessor, according to at leastone embodiment;

FIG. 19 illustrates a multi-graphics processing unit (GPU) system,according to at least one embodiment;

FIG. 20 illustrates a graphics processor, according to at least oneembodiment;

FIG. 21 is a block diagram illustrating a processor micro-architecturefor a processor, according to at least one embodiment;

FIG. 22 illustrates at least portions of a graphics processor, accordingto one or more embodiments;

FIG. 23 illustrates at least portions of a graphics processor, accordingto one or more embodiments;

FIG. 24 illustrates at least portions of a graphics processor, accordingto one or more embodiments;

FIG. 25 is a block diagram of a graphics processing engine of a graphicsprocessor in accordance with at least one embodiment;

FIG. 26 is a block diagram of at least portions of a graphics processorcore, according to at least one embodiment;

FIGS. 27A and 27B illustrate thread execution logic including an arrayof processing elements of a graphics processor core according to atleast one embodiment;

FIG. 28 illustrates a parallel processing unit (“PPU”), according to atleast one embodiment;

FIG. 29 illustrates a general processing cluster (“GPC”), according toat least one embodiment;

FIG. 30 illustrates a memory partition unit of a parallel processingunit (“PPU”), according to at least one embodiment;

FIG. 31 illustrates a streaming multi-processor, according to at leastone embodiment;

FIG. 32 illustrates a network for communicating data within a 5Gwireless communications network, according to at least one embodiment;

FIG. 33 illustrates a network architecture for a 5G LTE wirelessnetwork, according to at least one embodiment;

FIG. 34 is a diagram illustrating some basic functionality of a mobiletelecommunications network/system operating in accordance with LTE and5G principles, according to at least one embodiment;

FIG. 35 illustrates a radio access network which may be part of a 5Gnetwork architecture, according to at least one embodiment;

FIG. 36 provides an example illustration of a 5G mobile communicationssystem in which a plurality of different types of devices is used,according to at least one embodiment;

FIG. 37 illustrates an example high level system, according to at leastone embodiment;

FIG. 38 illustrates an architecture of a system of a network, accordingto at least one embodiment;

FIG. 39 illustrates example components of a device, according to atleast one embodiment;

FIG. 40 illustrates example interfaces of baseband circuitry, accordingto at least one embodiment;

FIG. 41 illustrates an example of an uplink channel, according to atleast one embodiment;

FIG. 42 illustrates an architecture of a system of a network, accordingto at least one embodiment;

FIG. 43 illustrates a control plane protocol stack, according to atleast one embodiment;

FIG. 44 illustrates a user plane protocol stack, according to at leastone embodiment;

FIG. 45 illustrates components of a core network, according to at leastone embodiment; and

FIG. 46 illustrates components of a system to support network functionvirtualization (NFV), according to at least one embodiment.

DETAILED DESCRIPTION

FIG. 1 is a block diagram illustrating a fifth-generation (5G) new radio(NR) signal processing environment 100, including de-rate-matching 102and scrambling demodulation 104, according to at least one embodiment.In at least one embodiment, scrambling demodulation 104 is referred toas descrambling. In at least one embodiment, at least one ofde-rate-matching 102 and scrambling demodulation 104 are performed in aparallel manner, such as described with respect to at least one of FIGS.3-6. In at least one embodiment, at least one of de-rate-matching 102and scrambling demodulation 104 are performed by at least one circuit,at least one system, at least one processor, at least one graphicsprocessing unit, at least one parallel processor, and/or at least someother processor or component thereof described and/or shown herein. Inat least one embodiment, at least some of 5G NR signal processingenvironment 100 is included in a virtual radio access network (vRAN). Inat least one embodiment, 5G NR signal processing environment 100includes a 5G vRAN stack 106 with a low physical (PHY) layer 108, a highPHY layer 110, a Medium Access Control (MAC) layer 112, a Radio LinkControl (RLC) layer 114, and a Packet Data Convergence Protocol (PDCP)layer 116. In at least one embodiment, low PHY layer 108 and high PHYlayer 110 are referred to as a PHY layer, rather than being referred toseparately. In at least one embodiment, 5G vRAN stack 106 communicateswith at least one user equipment (UE) 118, shown as UE1 to UEn, via aradio frequency (RF) layer 120 and wireless channels 122. In at leastone embodiment, 5G vRAN stack 106 communicates with a 5G Packet Core 124using internet protocol (IP) packets.

In at least one embodiment, low PHY layer 108 and high PHY layer 110include signal processing components 126, shown in an expanded blockdiagram between an analog to digital converter (ADC)/digital to analogconverter (DAC) 128 and MAC layer 112. In at least one embodiment, anuplink path 130 includes orthogonal frequency division multiplexing(OFDM) demodulation 132, receiver (Rx) beamforming 134, channelestimation 136, channel equalization 138, scrambling demodulation 104,de-rate-matching 102, low density parity check (LDPC) decoding 140, andtransport block cyclic redundancy check (CRC) 142. In at least oneembodiment, a downlink path 144 includes CRC segmentation 146, LDPCencoding 148, rate matching 150, scrambling modulation 152, precoding154, transmission (Tx) beamforming 156, and OFDM modulation 158. In atleast one embodiment uplink PHY layers run as a virtual network function(VNF). In at least one embodiment, VNF running uplink PHY layers runs ona cluster computing environment. In at least one embodiment, uplink PHYlayers process data relating to multiple-input multiple-output (MIMO)layers. In at least one embodiment, uplink path 130 includes resourcedemapping.

FIG. 2 is a block diagram illustrating a 5G NR physical uplink sharedchannel (PUSCH) processing pipeline 200, according to at least oneembodiment. In at least one embodiment, 5G NR PUSCH processing pipeline200 includes layer demapping 202, and descrambling and de-rate-matching204. In at least one embodiment, descrambling and de-rate-matching 204corresponds to scrambling demodulation 104 and de-rate-matching 102 ofFIG. 1. In at least one embodiment, at least one of layer demapping 202,and descrambling and de-rate-matching 204 are performed in a parallelmanner, such as described with respect to at least one of FIGS. 3-6. Inat least one embodiment, at least one aspect of 5G NR PUSCH processingpipeline 200 corresponds to at least one aspect of uplink path 130 ofFIG. 1. In at least one embodiment, at least one of layer demapping 202and descrambling and de-rate-matching 204 are performed by at least onecircuit, at least one system, at least one processor, at least onegraphics processing unit, at least one parallel processor, and/or atleast some other processor or component thereof described and/or shownherein.

In at least one embodiment, an RF layer 206 receives signals from atleast one antenna 208. In at least one embodiment, RF layer 206 receivessignals from multiple antennas 208. In at least one embodiment, antennas208 provide for reception of 5G NR MIMO signals. In a least oneembodiment, antennas 208 are 5G NR antennas. In at least one embodiment,PUSCH processing pipeline 200 includes performing a fast fouriertransform (FFT) and cyclic prefix (CP) removal at a block 210. In atleast one embodiment, PUSCH processing pipeline 200 includes Rxbeamforming in a midband at a block 212. In at least one embodiment,PUSCH processing pipeline 200 includes demodulation reference signal(DMRS) resource element (RE) demapping at a block 214. In at least oneembodiment, PUSCH processing pipeline 200 includes orthogonal cover code(OCC) removal at a block 216. In at least one embodiment, PUSCHprocessing pipeline 200 includes interpolation at a block 218 based, atleast in part, on an output of OCC removal 216 and precalculating a DMRinterpolation filter at a block 220. In at least one embodiment, PUSCHprocessing pipeline 200 includes an equalizer filter calculation at ablock 222. In at least one embodiment, PUSCH processing pipeline 200includes PUSCH RE demapping at a block 224 and equalization at a block226 based, at least in part, on an output of PUSCH RE demapping 224 andequalizer filter calculation 222. In at least one embodiment, channelestimation 136 of FIG. 1 includes at least one element of PUSCHprocessing pipeline 200, such as DMRS RE demapping 214, OCC removal 216,interpolation 218, and precalculate DMRS interpolation filter 220. In atleast one embodiment, channel equalization 138 of FIG. 1 includes atleast one element of PUSCH processing pipeline 200, such as PUSCH REdemapping 224 and equalization 226.

In at least one embodiment, PUSCH processing pipeline 200 includes softdemapping 228. In at least one embodiment, PUSCH processing pipeline 200performs layer demapping 202 based, at least in part, on an output ofsoft demapping 228. In at least one embodiment, PUSCH processingpipeline 200 includes LDPC decoding 230 based, at least in part, on anoutput of descramblind and de-rate-matching 204. In at least oneembodiment, PUSCH processing pipeline 200 includes channel bonding (CB),carrier aggregation, and cyclic redundancy check (CRC) at a block 232.In at least one embodiment, PUSCH processing pipeline 200 provides anoutput of block 232 to upper layers 234.

FIG. 3 is a block diagram illustrating layer demapping andde-rate-matching 300, according to at least one embodiment. In at leastone embodiment, layer demapping and de-rate-matching 300 is performed byat least one circuit, at least one system, at least one processor, atleast one graphics processing unit, at least one parallel processor,and/or at least some other processor or component thereof describedand/or shown herein. In at least one embodiment, 5G NR signal data isstored according to a layer-time-frequency resource grid 302 in an inputmemory array layout 304. In at least one embodiment, 5G NR signal dataof input memory array layout 304 corresponds to signal informationreceived from multiple antennas, such as antennas 208 of FIG. 2, at apoint in a PUSCH processing pipeline after soft demapping, such as softdemapping 228, has been performed. In at least one embodiment, 5G NRsignal data of layer-time-frequency resource grid 302 is referred to asinformation received from a plurality of 5G NR antennas. In at least oneembodiment, 5G signal data of layer-time-frequency resource grid 302 isreferred to as information transmitted using a plurality of 5G NRsignals. In at least one embodiment, layers of layer-time-frequencyresource grid 302 refer to MIMO layers. In at least one embodiment,layer demapping refers to demapping data of MIMO layers.

In at least one embodiment, layer demapping and de-rate-matching 300takes an equalizer soft demapping output and processes it for furtherhandling by a LDPC decoder. In at least one embodiment, data oflayer-time-frequency resource grid 302 and input memory array layout 304is stored in transport blocks, shown as TB 1 to TB N. In at least oneembodiment, data of input memory array layout 304 includes datacorresponding to log-likelihood ratios (LLRs). In at least oneembodiment, data of input memory array layout 304 includes floatingpoint values that represent LLRs. In at least one embodiment, data ofinput memory array layout 304 includes integer values that representquantizations of LLRs. In at least one embodiment, data of input memoryarray layout 304 includes soft bits. In at least one embodiment, data ofinput memory array layout 304 is a vector of LLRs.

In at least one embodiment, layer demapping, such as layer demapping 202of FIG. 2, includes extracting transport blocks 306 from data in inputmemory array layout 304 stored according to layer-time-frequencyresource grid 302 at a transport block extraction 308. In at least oneembodiment, transport blocks are segmented into multiple code blocks. Ina least one embodiment, layer demapping includes extracting code blocks310 from transport blocks 306 at a code block extraction 312, with aportion of extracted code blocks of TB 1 shown for clarity. In at leastone embodiment, layer demapping includes performing transport blockextraction and code block extraction in a combined fashion, such thatcode blocks 310 are extracted from data of input memory array layout304. In at least one embodiment, code blocks contain data representingQuadrature Amplitude Modulation (QAM) symbols. In at least oneembodiment, code blocks contain data representing some other type ofsymbol, such as Frequency Quadrature Amplitude Modulation (FQAM)symbols. In at least one embodiment, each code block includes one QAMsymbol.

In at least one embodiment, layer demapping and de-rate-matching 300includes descrambling code blocks 310 to generate descrambled codeblocks 314, with one descrambled code block CB i shown for clarity, at adescrambling 316. In at least one embodiment, descrambling 316corresponds to at least one of scrambling demodulation 104 of FIG. 1 ordescrambling of descrambling and de-rate-matching 204 of FIG. 2. In atleast one embodiment, layer demapping and de-rate-matching 300 includesblock de-interleaving descrambled code blocks 314 to generatede-interleaved code blocks 318, with one de-interleaved code block CBishown for clarity, at a de-interleaving 320. In at least one embodiment,de-interleaving 320 uses a block de-interleaver that operates on eachcode block.

In at least one embodiment, layer demapping and de-rate-matching 300includes rate expansion and filler bit insertion with respect tode-interleaved code blocks 318 to generate de-rate-matched code blocks322, shown with one expanded code block Exp CBi for clarity, at a rateexpansion 324. In at least one embodiment, rate expansion 324corresponds to at least one of de-rate-matching 102 of FIG. 1 orde-rate-matching of descrambling and de-rate-matching 204 of FIG. 2. Inat least one embodiment, at least one of de-rate-matched code blocks 322include filler bits 326. In at least one embodiment, at least one ofde-rate-matched code blocks 322 include padding, such as zero padding,not shown for clarity. In at least one embodiment, rate expansion 324includes expanding a rate-matched (punctured) codeword to a full lengthcodeword by writing LLRs in corresponding positions, padding with zerosfor punctured bits, and adding a predetermined value for filler bitpositions.

In at least one embodiment, layer demapping and de-rate-matching 300includes soft combining de-rate-matched code blocks 322 with previouslystored corresponding code blocks 328 of a hybrid automatic repeatrequest (HARQ) buffer to generate combined code blocks 330 in HARQbuffer, shown with one code block 328 before combination and onecombined code block 330 after combination in HARQ buffer for clarity, ata soft combining 332. In at least one embodiment, soft combining 332occurs as a part of de-rate-matching 102 of FIG. 1 or as a part ofde-rate-matching of descrambling and de-rate-matching 204 of FIG. 2, socombined code blocks 330 can be used for LDPC decoding. In at least oneembodiment, previously stored corresponding code blocks 328 includefiller bits 334. In at least one embodiment, filler bits 334 are in adifferent location than filler bits 326. In at least one embodiment,filler bits 334 and filler bits 326 are both present in combined codeblocks 330, as shown. In at least one embodiment, soft combining 332includes adding values of data in de-rate-matched code blocks 322 tovalues of data in previously stored corresponding code blocks 328. In atleast one embodiment, values in HARQ buffer are changed in place by a+=operation during soft combining 332. In at least one embodiment, softcombining 332 includes combining LLRs with HARQ buffer contents, whichmay contain LLRs received in previous HARQ transmissions. In at leastone embodiment, layer demapping and de-rate-matching 300 uses agather/scatter technique, with reads from input memory array layout 304using a gather operation and writes to an output buffer during softcombining 332 using a scatter operation.

In at least one embodiment, at least one parallel processor performslayer demapping and de-rate-matching 300 in parallel. In at least oneembodiment, at least some aspects of layer demapping andde-rate-matching 300 are performed by software running on a graphicsprocessing unit (GPU). In at least one embodiment, a plurality of threadblocks, each with a plurality of threads, perform layer demapping andde-rate-matching 300 in parallel. In at least one embodiment, a threadblock is referred to as a group of threads. In at least one embodiment,each code block is handled by a different thread block. In at least oneembodiment, if a code block includes more elements, such as LLRs, than amaximum number of available threads in an initially assigned threadblock, at least one additional thread block is assigned to handleelements that exceed maximum number of available threads in initiallyassigned thread block. In at least one embodiment, if a code blockincludes more elements, such as LLRs, than a maximum number of availablethreads in an initially assigned thread block, threads of initiallyassigned thread block will loop through elements that exceed maximumnumber of available threads such that more than one subset of code blockelements are sequentially handled in parallel by threads of initiallyassigned thread block.

In at least one embodiment, for each code block, a corresponding threadblock executes transport block extraction 308, code block extraction312, descrambling 316, de-interleaving 320, rate expansion 324, and softcombining 332. In at least one embodiment, for a thread block designatedas i, a thread reads in[j] and writes out[k]+=s[m]*in[j], withk=mapping_function(j, <parameters>), where out[k] is HARQ buffer or agiven HARQ process, s[j] is in a set {+1, −1}, and <parameters> includesinput index, thread block indices (which map to code block index andtransport block index), transport block size, number of multiple inputmultiple output (MIMO) layers, modulation index, code rate, code basegraph, and redundancy version. In at least one embodiment, a subset of<parameters> and/or additional parameters are used. In at least oneembodiment, k is used to layer demap j. In at least one embodiment,mapping_function is referred to as a demapping function. In at least oneembodiment, mapping_function is referred to as a layer demappingfunction. In at least one embodiment, [j] refers to an array of valuesthat correspond to LLRs. In at least one embodiment, [j] corresponds todata of at least one of layer-time-frequency resource grid 302, inputmemory array layout 402 of FIG. 4, and input buffer 502 of FIG. 5. In atleast one embodiment, out[k] corresponds to at least one of HARQ bufferdescribed with respect to previously stored corresponding code blocks328 and combined code blocks 330, output memory array layout 404 of FIG.4, and output buffer 506 of FIG. 5.

In at least one embodiment, thread block indices include a first threadblock index corresponding to an x dimension of a two-dimensional threadblock array, and a second thread block index corresponding to a ydimension of a two-dimensional thread block array. In at least oneembodiment, a thread index that identifies particular threads within athread block is used as a parameter in at least one of <parameters> and<scr_parameters>. In at least one embodiment, at least one of<parameters> include at least one aspect described in a Third GenerationPartnership Project (3GPP) technical specification (TS), such as TS38.212, TS 38.211, and/or TS 38.214 Release 15, version 15.6.0, or someother version and/or release.

In at least one embodiment, descrambling 316 is performed by changing asign of in[j] based on a scrambling sequence s[m], withm=scr_mapping_function(j, <scr_parameters>). In at least one embodiment,s[m] is a pseudorandom sequence based, at least in part, onscr_mapping_function. In at least one embodiment, scr_mapping_functionis referred to as a descrambling function. In at least one embodiment,changing a sign of in[j] includes multiplying in[j] by negative one inresponse to s[m] indicates sign of in[j] is to be changed. In at leastone embodiment, leaving a sign of in[j] at a same value includesmultiplying in[j] by positive one in response to s[m] indicates sign ofin[j] is not to be changed. In at least one embodiment, <scr_parameters>has same parameters as <parameters>, described with respect tomapping_function. In at least one embodiment, <scr_parameters> is asubset of <parameters>, such as by including all parameters of<parameters> except redundancy version. In some embodiments,<scr_parameters> includes at least one parameter not included in<parameters>. In at least one embodiment, soft combining 332 isperformed when in[j] is accumulated into out[k]. In at least oneembodiment, out[k] is initialized to zero.

In at least one embodiment, at least one circuit of at least oneprocessor, system, and/or other device described herein causesinformation received from a plurality of 5G NR antennas, such asantennas 208, to be decoded in parallel by a corresponding plurality ofprocessor pipelines. In at least one embodiment, information receivedfrom plurality of 5G NR antennas has been processed by soft demapping,such as soft demapping 228, and decoding information includes at leastone aspect of layer demapping and de-rate-matching 300, such as layerdemapping, descrambling, and de-rate-matching. In at least oneembodiment, corresponding plurality of processor pipelines refers tousing at least one thread block per code block to perform layerdemapping and de-rate-matching 300. In at least one embodiment,instructions stored on a machine-readable medium, when performed, causea parallel processor to cause information transmitted using a pluralityof 5G NR signals, such as data of layer-time-frequency resource grid302, to be decoded by parallel processor by scheduling a plurality ofthread groups each corresponding to at least one of plurality of 5G NRsignals on parallel processor. In at least one embodiment, causinginformation transmitted using a plurality of 5G NR signals to be decodedincludes at least one aspect of layer demapping and de-rate-matching300, such as layer demapping, descrambling, and de-rate-matching. In atleast one embodiment, plurality of thread groups each corresponding toat least one of plurality of 5G NR signals refers to using at least onethread block per code block to perform layer demapping andde-rate-matching 300.

FIG. 4 is a block diagram illustrating layer demapping andde-rate-matching with single read/write operations 400, according to atleast one embodiment. In at least one embodiment, layer demapping andde-rate-matching with single read/write operations 400 is performed byat least one circuit, at least one system, at least one processor, atleast one graphics processing unit, at least one parallel processor,and/or at least some other processor or component thereof describedand/or shown herein. In at least one embodiment, threads of threadblocks read data elements from an input memory array layout 402, performlayer demapping and de-rate-matching operations, and write to an outputmemory array layout 404. In at least one embodiment, input memory arraylayout 402 corresponds to input memory array layout 304 of FIG. 3. In atleast one embodiment, output memory array layout 404 corresponds to aHARQ buffer layout, such as where combined code block 330 of FIG. 3 iswritten. In at least one embodiment, input memory array layout 402 isreferred to as a first mapping configuration and output memory arraylayout 404 is referred to as a second mapping configuration.

In at least one embodiment, N=Σ_(i=1) ^(Ntb) C(i) thread blocks processa workload of layer demapping and de-rate-matching with singleread/write operations 400. In at least one embodiment, threads of athread block 406 read data elements of a first code block from inputmemory array layout 402, and write a transformed first code block tooutput memory array layout 404, shown as CB 1 in TB 1. In at least oneembodiment, threads of a thread block 408 read data elements of a secondcode block from input memory array layout 404, and write a transformedsecond code block to output memory array layout 404, shown as CB 1 in TB2. In at least one embodiment, data elements of input memory arraylayout 402 read by threads of thread block 406 and threads of threadblock 408 are values corresponding to LLRs, such as floating point LLRvalues. In at least one embodiment, threads of thread block 406 run inparallel. In at least one embodiment, threads of thread block 408 run inparallel. In at least one embodiment, thread block 406 and thread block408 run in parallel.

In at least one embodiment, threads of thread block 406 and threads ofthread block 408 perform layer demapping and de-rate-matching, such asdescribed with respect to layer demapping and de-rate-matching 300 ofFIG. 3. In at least one embodiment, at least one thread of thread block406 and/or at least one thread of thread block 408 performs an actionthat does not involve a read from input memory array layout 402, such aswriting a filler bit or a padding zero to output memory array layout404. In at least one embodiment, at least one of thread block 406 andthread block 408 are started with a kernel launch function. In at leastone embodiment, kernel launch function launches thread block 406 andthread block 408 by passing at least one parameter from <parameters> and<scr_parameters> discussed with respect to layer demapping andde-rate-matching 300 of FIG. 3. In at least one embodiment, kernellaunch function calculates at least one parameter of <parameters> and<scr_parameters> used for indexing of at least one of thread blocks andthreads. In at least one embodiment, a single read and write operationfrom global memory is executed for each element (LLR floating pointvalue). In at least one embodiment, with a single read-write operation,all of transport block extraction 308, code block extraction 312,descrambling 316, de-interleaving 320, rate expansion 324, and softcombining 332 are executed.

In at least one embodiment, a subset of threads insert filler bits aspart of rate expansion 324. In at least one embodiment, a number offiller bits, F, is a parameter provided to a kernel by a kernel launchfunction. In at least one embodiment, a number of filler bits iscalculated using a number of bits in a code block, K′, based, at leastin part, on a difference between a number of information bits, K,corresponding to a chosen LDPC lifting size Zc, and K′. In at least oneembodiment, number of filler bits is calculated by a central processingunit (CPU) and passed to a parallel processor such as a GPU using akernel launch function. In at least one embodiment, a parallelprocessor, such as a GPU, calculates number of filler bits. In at leastone embodiment, a CPU calculates a pointer to where each code blockstarts in input memory array layout 402, and passes pointer to aparallel processor, such as a GPU, as a startIndex parameter using akernel launch function. In at least one embodiment, a parallelprocessor, such as a GPU, calculates startIndex. In at least oneembodiment, a CPU calculates a code block size, E, and passes E to aparallel processor, such as a GPU, using a kernel launch function. In atleast one embodiment, a parallel processor, such as a GPU, calculates E.

FIG. 5 is a block diagram illustrating layer demapping andde-rate-matching with shared memory operations 500, according to atleast one embodiment. In at least one embodiment, layer demapping andde-rate-matching with shared memory operations 500 is performed by atleast one circuit, at least one system, at least one processor, at leastone graphics processing unit, at least one parallel processor, and/or atleast some other processor or component thereof described and/or shownherein. In at least one embodiment, data elements in an input buffer 502are read into a shared memory buffer 504 in a shared memory by multiplethreads in parallel. In at least one embodiment, input buffer 502 has aninput memory array layout that corresponds to at least one of inputmemory array layout 402 or input memory array layout 304. In at leastone embodiment, input buffer 502 is in global memory. In at least oneembodiment, input buffer 502 is in local memory. In at least oneembodiment, a first thread, shown as threadIdx.x=0 (also referred to asthread 0), reads a first data element from a first position of inputbuffer 502 into a first position of shared memory buffer 504. In atleast one embodiment, a second thread, shown as threadIdx.x=1 (alsoreferred to as thread 1), reads a second data element from a secondposition of input buffer 502 into a second position of shared memorybuffer 504. In at least one embodiment, thread 0 and thread 1 readcontiguous positions from global memory. In at least one embodiment, athread block, including thread 0 and thread 1, reads N_tbsz contiguouselements of input buffer 502. In at least one embodiment, N refers to anumber of thread blocks and tbsz refers to a thread block size. In atleast one embodiment, N_tbsz refers to a number of elements in a totalof N thread blocks. In at least one embodiment, N_tbsz refers to anumber of elements in a particular thread block. In at least oneembodiment, threads are synchronized after reading data elements intoshared memory buffer 504. In at least one embodiment, threads aresynchronized with a_syncthreads( ) operation, although it should beunderstood that other thread synchronization techniques may be used inother embodiments.

In at least one embodiment, threads of thread blocks layer demap andde-rate-match data elements of code blocks after data elements have beenread into shared memory buffer 504. In at least one embodiment, threadsof thread blocks perform at least one of transport block extraction 308,code block extraction 312, descrambling 316, de-interleaving 320, rateexpansion 324, and soft combining 332 using data elements read intoshared memory buffer 504. In at least one embodiment, threadIdx.x=0reads from a first position in shared memory buffer 504, performs atleast one of transport block extraction 308, code block extraction 312,descrambling 316, de-interleaving 320, rate expansion 324, and softcombining 332, and writes into a first position of a first transportblock, TB 1, in an output buffer 506. In at least one embodiment, outputbuffer 506 is in global memory. In at least one embodiment, outputbuffer 506 is a HARQ buffer. In at least one embodiment, output buffer506 corresponds to output memory array layout 404. In at least oneembodiment, threadIdx.x=1 reads from a position in shared memory buffer504, performs at least one of transport block extraction 308, code blockextraction 312, descrambling 316, de-interleaving 320, rate expansion324, and soft combining 332, and writes into a second position of TB 1in output buffer 506. In at least one embodiment, threadIdx.x=1 readsfrom position 0+Σ_(i=1) ^(N) Qm(i) in shared memory buffer 504 andwrites into second position of TB 1. In at least one embodiment, thread0 and thread 1 write to contiguous positions on global memory. In atleast one embodiment, a thread block writes N_tbsz block-wise contiguouselements (one element out of every group of sum (Qm) elements).

In at least one embodiment, threads of thread blocks, such as thread 0and thread 1, perform transport block extraction 308 and code blockextraction 312 when reading from input buffer 502 into shared memorybuffer 504. In at least one embodiment, threads of thread blocks, suchas thread 0 and thread 1, perform descrambling 316, de-interleaving 320,and rate expansion 324 with respect to data elements in shared memorybuffer 504. In at least one embodiment, threads of thread blocks, suchas thread 0 and thread 1, perform soft combining 332 when writing tooutput buffer 506.

In at least one embodiment, a block of elements (LLR floating pointvalues) is read into shared memory buffer 504 in shared memory, takingadvantage of a coalesced global memory read operation. In at least oneembodiment, a size of block of elements is equal to a thread block size.In at least one embodiment, each thread executes transport blockextraction 308, code block extraction 312, descrambling 316,de-interleaving 320, rate expansion 324, and soft combining 332, andwrites to global memory, such as to output buffer 506, taking advantageof a coalesced global memory write operation. In at least oneembodiment, using coalesced global memory read and write operations incombination with shared memory results in faster processing times. In atleast one embodiment, threads write contiguously in shared memory, suchas shared memory buffer 504, and read non-contiguously from sharedmemory. In at least one embodiment, threads write non-contiguously inshared memory, such as shared memory buffer 504, and read contiguouslyfrom shared memory.

FIG. 6 illustrates a flowchart of a technique 600 of layer demapping andde-rate-matching, according to at least one embodiment. In at least oneembodiment, technique 600 is performed by at least one circuit, at leastone system, at least one processor, at least one graphics processingunit, at least one parallel processor, and/or at least some otherprocessor or component thereof described and/or shown herein. In atleast one embodiment, multiple threads of at least one thread blockperform at least one aspect of technique 600 in parallel. In at leastone embodiment, technique 600 includes, at a block 602, receivinginformation from a plurality of 5G NR antennas, such as antennas 208. Inat least one embodiment, at a block 604, technique 600 includesextracting transport blocks and code blocks from received information.In at least one embodiment, extracting transport blocks and code blocksform received information corresponds to transport block extraction 308and code block extraction 312 of FIG. 3.

In at least one embodiment, at a block 606, technique 600 includes layerdemapping extracted code blocks. In at least one embodiment, layerdemapping at block 606 corresponds to layer demapping 202 of FIG. 2. Inat least one embodiment, at a block 608, technique 600 includesdescrambling code blocks. In at least one embodiment, descrambling codeblocks at block 608 corresponds to at least one of descrambling 316,descrambling of descrambling and de-rate-matching 204, and scramblingdemodulation 104 of FIGS. 1-3. In at least one embodiment, at a block610, technique 600 includes de-interleaving code blocks. In at least oneembodiment, de-interleaving code blocks at block 610 corresponds tode-interleaving 320 of FIG. 3. In at least one embodiment, at a block612, technique 600 includes de-rate-matching code blocks. In at leastone embodiment, de-rate-matching code blocks at block 612 corresponds toat least one of de-rate matching 102, de-rate-matching of descramblingand de-rate-matching 204, and rate expansion 324 of FIGS. 1-3. In atleast one embodiment, at a block 614, technique 600 includessoft-combining layer demapped, descrambled, de-interleaved,de-rate-matched code blocks with HARQ buffer contents. In at least oneembodiment, soft-combining at block 614 corresponds to soft combining332 of FIG. 3. In at least one embodiment, at a block 616, technique 600includes performing other actions.

Data Center

FIG. 7 illustrates an example data center 700, in which at least oneembodiment may be used. In at least one embodiment, data center 700includes a data center infrastructure layer 710, a framework layer 720,a software layer 730 and an application layer 740.

In at least one embodiment, as shown in FIG. 7, data centerinfrastructure layer 710 may include a resource orchestrator 712,grouped computing resources 714, and node computing resources (“nodeC.R.s”) 716(1)-716(N), where “N” represents any whole, positive integer.In at least one embodiment, node C.R.s 716(1)-716(N) may include, butare not limited to, any number of central processing units (“CPUs”) orother processors (including accelerators, field programmable gate arrays(FPGAs), graphics processors, etc.), memory devices (e.g., dynamicread-only memory), storage devices (e.g., solid state or disk drives),network input/output (“NW I/O”) devices, network switches, virtualmachines (“VMs”), power modules, and cooling modules, etc. In at leastone embodiment, one or more node C.R.s from among node C.R.s716(1)-716(N) may be a server having one or more of above-mentionedcomputing resources.

In at least one embodiment, grouped computing resources 714 may includeseparate groupings of node C.R.s housed within one or more racks (notshown), or many racks housed in data centers at various geographicallocations (also not shown). separate groupings of node C.R.s withingrouped computing resources 714 may include grouped compute, network,memory or storage resources that may be configured or allocated tosupport one or more workloads. In at least one embodiment, several nodeC.R.s including CPUs or processors may grouped within one or more racksto provide compute resources to support one or more workloads. In atleast one embodiment, one or more racks may also include any number ofpower modules, cooling modules, and network switches, in anycombination.

In at least one embodiment, resource orchestrator 712 may configure orotherwise control one or more node C.R.s 716(1)-716(N) and/or groupedcomputing resources 714. In at least one embodiment, resourceorchestrator 712 may include a software design infrastructure (“SDI”)management entity for data center 700. In at least one embodiment,resource orchestrator may include hardware, software or some combinationthereof.

In at least one embodiment, as shown in FIG. 7, framework layer 720includes a job scheduler 732, a configuration manager 734, a resourcemanager 736 and a distributed file system 738. In at least oneembodiment, framework layer 720 may include a framework to supportsoftware 732 of software layer 730 and/or one or more application(s) 742of application layer 740. In at least one embodiment, software 732 orapplication(s) 742 may respectively include web-based service softwareor applications, such as those provided by Amazon Web Services, GoogleCloud and Microsoft Azure. In at least one embodiment, framework layer720 may be, but is not limited to, a type of free and open-sourcesoftware web application framework such as Apache Spark™ (hereinafter“Spark”) that may utilize distributed file system 738 for large-scaledata processing (e.g., “big data”). In at least one embodiment, jobscheduler 732 may include a Spark driver to facilitate scheduling ofworkloads supported by various layers of data center 700. In at leastone embodiment, configuration manager 734 may be capable of configuringdifferent layers such as software layer 730 and framework layer 720including Spark and distributed file system 738 for supportinglarge-scale data processing. In at least one embodiment, resourcemanager 736 may be capable of managing clustered or grouped computingresources mapped to or allocated for support of distributed file system738 and job scheduler 732. In at least one embodiment, clustered orgrouped computing resources may include grouped computing resource 714at data center infrastructure layer 710. In at least one embodiment,resource manager 736 may coordinate with resource orchestrator 712 tomanage these mapped or allocated computing resources.

In at least one embodiment, software 732 included in software layer 730may include software used by at least portions of node C.R.s716(1)-716(N), grouped computing resources 714, and/or distributed filesystem 738 of framework layer 720. In at least one embodiment, one ormore types of software may include, but are not limited to, Internet webpage search software, e-mail virus scan software, database software, andstreaming video content software.

In at least one embodiment, application(s) 742 included in applicationlayer 740 may include one or more types of applications used by at leastportions of node C.R.s 716(1)-716(N), grouped computing resources 714,and/or distributed file system 738 of framework layer 720. one or moretypes of applications may include, but are not limited to, any number ofa genomics application, a cognitive compute, and a machine learningapplication, including training or inferencing software, machinelearning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.) orother machine learning applications used in conjunction with one or moreembodiments.

In at least one embodiment, any of configuration manager 734, resourcemanager 736, and resource orchestrator 712 may implement any number andtype of self-modifying actions based on any amount and type of dataacquired in any technically feasible fashion. In at least oneembodiment, self-modifying actions may relieve a data center operator ofdata center 700 from making possibly bad configuration decisions andpossibly avoiding underutilized and/or poor performing portions of adata center.

In at least one embodiment, data center 700 may include tools, services,software or other resources to train one or more machine learning modelsor predict or infer information using one or more machine learningmodels according to one or more embodiments described herein. Forexample, in at least one embodiment, a machine learning model may betrained by calculating weight parameters according to a neural networkarchitecture using software and computing resources described above withrespect to data center 700. In at least one embodiment, trained machinelearning models corresponding to one or more neural networks may be usedto infer or predict information using resources described above withrespect to data center 700 by using weight parameters calculated throughone or more training techniques described herein.

In at least one embodiment, data center may use CPUs,application-specific integrated circuits (ASICs), GPUs, FPGAs, or otherhardware to perform training and/or inferencing using above-describedresources. Moreover, one or more software and/or hardware resourcesdescribed above may be configured as a service to allow users to trainor performing inferencing of information, such as image recognition,speech recognition, or other artificial intelligence services.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 7 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one of grouped computing resources 714 and nodeC.R. 716 are used to cause information received from a plurality of 5Gnew radio antennas to be decoded in parallel by a plurality of processorpipelines. In at least one embodiment, at least one of grouped computingresources 714 and node C.R. 716 are used to layer demap, descramble, andde-rate-match 5G NR PUSCH data that has been soft demapped inpreparation for LDPC decoding, where at least one thread block is usedto perform operations on code block data elements (e.g., LLRs) inparallel.

Autonomous Vehicle

FIG. 8A illustrates an example of an autonomous vehicle 800, accordingto at least one embodiment. In at least one embodiment, autonomousvehicle 800 (alternatively referred to herein as “vehicle 800”) may be,without limitation, a passenger vehicle, such as a car, a truck, a bus,and/or another type of vehicle that accommodates one or more passengers.In at least one embodiment, vehicle 800 may be a semi-tractor-trailertruck used for hauling cargo. In at least one embodiment, vehicle 800may be an airplane, robotic vehicle, or other kind of vehicle.

Autonomous vehicles may be described in terms of automation levels,defined by National Highway Traffic Safety Administration (“NHTSA”), adivision of US Department of Transportation, and Society of AutomotiveEngineers (“SAE”) “Taxonomy and Definitions for Terms Related to DrivingAutomation Systems for On-Road Motor Vehicles” (e.g., Standard No.J3016-201806, published on Jun. 15, 2018, Standard No. J3016-201609,published on Sep. 30, 2016, and previous and future versions of thisstandard). In one or more embodiments, vehicle 800 may be capable offunctionality in accordance with one or more of level 1-level 5 ofautonomous driving levels. For example, in at least one embodiment,vehicle 800 may be capable of conditional automation (Level 3), highautomation (Level 4), and/or full automation (Level 5), depending onembodiment.

In at least one embodiment, vehicle 800 may include, without limitation,components such as a chassis, a vehicle body, wheels (e.g., 2, 4, 6, 8,18, etc.), tires, axles, and other components of a vehicle. In at leastone embodiment, vehicle 800 may include, without limitation, apropulsion system 850, such as an internal combustion engine, hybridelectric power plant, an all-electric engine, and/or another propulsionsystem type. In at least one embodiment, propulsion system 850 may beconnected to a drive train of vehicle 800, which may include, withoutlimitation, a transmission, to enable propulsion of vehicle 800. In atleast one embodiment, propulsion system 850 may be controlled inresponse to receiving signals from a throttle/accelerator(s) 852.

In at least one embodiment, a steering system 854, which may include,without limitation, a steering wheel, is used to steer a vehicle 800(e.g., along a desired path or route) when a propulsion system 850 isoperating (e.g., when vehicle is in motion). In at least one embodiment,a steering system 854 may receive signals from steering actuator(s) 856.steering wheel may be optional for full automation (Level 5)functionality. In at least one embodiment, a brake sensor system 846 maybe used to operate vehicle brakes in response to receiving signals frombrake actuator(s) 848 and/or brake sensors.

In at least one embodiment, controller(s) 836, which may include,without limitation, one or more system on chips (“SoCs”) (not shown inFIG. 8A) and/or graphics processing unit(s) (“GPU(s)”), provide signals(e.g., representative of commands) to one or more components and/orsystems of vehicle 800. For instance, in at least one embodiment,controller(s) 836 may send signals to operate vehicle brakes via brakeactuators 848, to operate steering system 854 via steering actuator(s)856, to operate propulsion system 850 via throttle/accelerator(s) 852.controller(s) 836 may include one or more onboard (e.g., integrated)computing devices (e.g., supercomputers) that process sensor signals,and output operation commands (e.g., signals representing commands) toenable autonomous driving and/or to assist a human driver in drivingvehicle 800. In at least one embodiment, controller(s) 836 may include afirst controller 836 for autonomous driving functions, a secondcontroller 836 for functional safety functions, a third controller 836for artificial intelligence functionality (e.g., computer vision), afourth controller 836 for infotainment functionality, a fifth controller836 for redundancy in emergency conditions, and/or other controllers. Inat least one embodiment, a single controller 836 may handle two or moreof above functionalities, two or more controllers 836 may handle asingle functionality, and/or any combination thereof.

In at least one embodiment, controller(s) 836 provide signals forcontrolling one or more components and/or systems of vehicle 800 inresponse to sensor data received from one or more sensors (e.g., sensorinputs). In at least one embodiment, sensor data may be received from,for example and without limitation, global navigation satellite systems(“GNSS”) sensor(s) 858 (e.g., Global Positioning System sensor(s)),RADAR sensor(s) 860, ultrasonic sensor(s) 862, LIDAR sensor(s) 864,inertial measurement unit (“IMU”) sensor(s) 866 (e.g., accelerometer(s),gyroscope(s), magnetic compass(es), magnetometer(s), etc.),microphone(s) 896, stereo camera(s) 868, wide-view camera(s) 870 (e.g.,fisheye cameras), infrared camera(s) 872, surround camera(s) 874 (e.g.,360 degree cameras), long-range cameras (not shown in FIG. 8A),mid-range camera(s) (not shown in FIG. 8A), speed sensor(s) 844 (e.g.,for measuring speed of vehicle 800), vibration sensor(s) 842, steeringsensor(s) 840, brake sensor(s) (e.g., as part of brake sensor system846), and/or other sensor types.

In at least one embodiment, one or more of controller(s) 836 may receiveinputs (e.g., represented by input data) from an instrument cluster 832of vehicle 800 and provide outputs (e.g., represented by output data,display data, etc.) via a human-machine interface (“HMI”) display 834,an audible annunciator, a loudspeaker, and/or via other components ofvehicle 800. In at least one embodiment, outputs may include informationsuch as vehicle velocity, speed, time, map data (e.g., a High Definitionmap (not shown in FIG. 8A), location data (e.g., vehicle's 800 location,such as on a map), direction, location of other vehicles (e.g., anoccupancy grid), information about objects and status of objects asperceived by controller(s) 836, etc. For example, in at least oneembodiment, HMI display 834 may display information about presence ofone or more objects (e.g., a street sign, caution sign, traffic lightchanging, etc.), and/or information about driving maneuvers vehicle hasmade, is making, or will make (e.g., changing lanes now, taking exit 34Bin two miles, etc.).

In at least one embodiment, vehicle 800 further includes a networkinterface 824 which may use wireless antenna(s) 826 and/or modem(s) tocommunicate over one or more networks. For example, in at least oneembodiment, network interface 824 may be capable of communication overLong-Term Evolution (“LTE”), Wideband Code Division Multiple Access(“WCDMA”), Universal Mobile Telecommunications System (“UMTS”), GlobalSystem for Mobile communication (“GSM”), IMT-CDMA Multi-Carrier(“CDMA2000”), etc. In at least one embodiment, wireless antenna(s) 826may also enable communication between objects in environment (e.g.,vehicles, mobile devices, etc.), using local area network(s), such asBluetooth, Bluetooth Low Energy (“LE”), Z-Wave, ZigBee, etc., and/or lowpower wide-area network(s) (“LPWANs”), such as LoRaWAN, SigFox, etc.

In at least one embodiment, signals received by antennas 208 of FIG. 2may be from vehicle 800 and processed as described with respect to atleast one of FIGS. 1-6 to provide information to vehicle 800 for itsautonomous operation, such as weather data, navigational data, roadcondition data, and/or may be used to provide a remote operator anability to control vehicle 800 remotely.

FIG. 8B illustrates an example of camera locations and fields of viewfor autonomous vehicle 800 of FIG. 8A, according to at least oneembodiment. In at least one embodiment, cameras and respective fields ofview are one example embodiment and are not intended to be limiting. Forinstance, in at least one embodiment, additional and/or alternativecameras may be included and/or cameras may be located at differentlocations on vehicle 800.

In at least one embodiment, camera types for cameras may include, butare not limited to, digital cameras that may be adapted for use withcomponents and/or systems of vehicle 800. Camera(s) may operate atautomotive safety integrity level (“ASIL”) B and/or at another ASIL. Inat least one embodiment, camera types may be capable of any imagecapture rate, such as 60 frames per second (fps), 1220 fps, 240 fps,etc., depending on embodiment. In at least one embodiment, cameras maybe capable of using rolling shutters, global shutters, another type ofshutter, or a combination thereof. In at least one embodiment, colorfilter array may include a red clear clear clear (“RCCC”) color filterarray, a red clear clear blue (“RCCB”) color filter array, a red bluegreen clear (“RBGC”) color filter array, a Foveon X3 color filter array,a Bayer sensors (“RGGB”) color filter array, a monochrome sensor colorfilter array, and/or another type of color filter array. In at least oneembodiment, clear pixel cameras, such as cameras with an RCCC, an RCCB,and/or an RBGC color filter array, may be used in an effort to increaselight sensitivity.

In at least one embodiment, one or more of camera(s) may be used toperform advanced driver assistance systems (“ADAS”) functions (e.g., aspart of a redundant or fail-safe design). For example, in at least oneembodiment, a Multi-Function Mono Camera may be installed to providefunctions including lane departure warning, traffic sign assist andintelligent headlamp control. In at least one embodiment, one or more ofcamera(s) (e.g., all of cameras) may record and provide image data(e.g., video) simultaneously.

In at least one embodiment, one or more of cameras may be mounted in amounting assembly, such as a custom designed (three-dimensional (“3D”)printed) assembly, in order to cut out stray light and reflections fromwithin car (e.g., reflections from dashboard reflected in windshieldmirrors) which may interfere with camera's image data capture abilities.With reference to wing-mirror mounting assemblies, in at least oneembodiment, wing-mirror assemblies may be custom 3D printed so thatcamera mounting plate matches shape of wing-mirror. In at least oneembodiment, camera(s) may be integrated into wing-mirror. In at leastone embodiment, for side-view cameras, camera(s) may also be integratedwithin four pillars at each corner of car.

In at least one embodiment, cameras with a field of view that includeportions of environment in front of vehicle 800 (e.g., front-facingcameras) may be used for surround view, to help identify forward facingpaths and obstacles, as well as aid in, with help of one or more ofcontrollers 836 and/or control SoCs, providing information critical togenerating an occupancy grid and/or determining preferred vehicle paths.In at least one embodiment, front-facing cameras may be used to performmany of same ADAS functions as LIDAR, including, without limitation,emergency braking, pedestrian detection, and collision avoidance. In atleast one embodiment, front-facing cameras may also be used for ADASfunctions and systems including, without limitation, Lane DepartureWarnings (“LDW”), Autonomous Cruise Control (“ACC”), and/or otherfunctions such as traffic sign recognition.

In at least one embodiment, a variety of cameras may be used in afront-facing configuration, including, for example, a monocular cameraplatform that includes a CMOS (“complementary metal oxidesemiconductor”) color imager. In at least one embodiment, wide-viewcamera 870 may be used to perceive objects coming into view fromperiphery (e.g., pedestrians, crossing traffic or bicycles). Althoughonly one wide-view camera 870 is illustrated in FIG. 8B, in otherembodiments, there may be any number (including zero) of wide-viewcamera(s) 870 on vehicle 800. In at least one embodiment, any number oflong-range camera(s) 898 (e.g., a long-view stereo camera pair) may beused for depth-based object detection, especially for objects for whicha neural network has not yet been trained. In at least one embodiment,long-range camera(s) 898 may also be used for object detection andclassification, as well as basic object tracking.

In at least one embodiment, any number of stereo camera(s) 868 may alsobe included in a front-facing configuration. In at least one embodiment,one or more of stereo camera(s) 868 may include an integrated controlunit comprising a scalable processing unit, which may provide aprogrammable logic (“FPGA”) and a multi-core micro-processor with anintegrated Controller Area Network (“CAN”) or Ethernet interface on asingle chip. In at least one embodiment, such a unit may be used togenerate a 3D map of environment of vehicle 800, including a distanceestimate for all points in image. In at least one embodiment, one ormore of stereo camera(s) 868 may include, without limitation, compactstereo vision sensor(s) that may include, without limitation, two cameralenses (one each on left and right) and an image processing chip thatmay measure distance from vehicle 800 to target object and use generatedinformation (e.g., metadata) to activate autonomous emergency brakingand lane departure warning functions. In at least one embodiment, othertypes of stereo camera(s) 868 may be used in addition to, oralternatively from, those described herein.

In at least one embodiment, cameras with a field of view that includeportions of environment to side of vehicle 800 (e.g., side-view cameras)may be used for surround view, providing information used to create andupdate occupancy grid, as well as to generate side impact collisionwarnings. For example, in at least one embodiment, surround camera(s)874 (e.g., four surround cameras 874 as illustrated in FIG. 8B) could bepositioned on vehicle 800. surround camera(s) 874 may include, withoutlimitation, any number and combination of wide-view camera(s) 870,fisheye camera(s), 360 degree camera(s), and/or like. For instance, inat least one embodiment, four fisheye cameras may be positioned onfront, rear, and sides of vehicle 800. In at least one embodiment,vehicle 800 may use three surround camera(s) 874 (e.g., left, right, andrear), and may leverage one or more other camera(s) (e.g., aforward-facing camera) as a fourth surround-view camera.

In at least one embodiment, cameras with a field of view that includeportions of environment to rear of vehicle 800 (e.g., rear-view cameras)may be used for park assistance, surround view, rear collision warnings,and creating and updating occupancy grid. In at least one embodiment, awide variety of cameras may be used including, but not limited to,cameras that are also suitable as a front-facing camera(s) (e.g.,long-range cameras 898 and/or mid-range camera(s) 876, stereo camera(s)868), infrared camera(s) 872, etc.), as described herein.

In at least one embodiment, signals received by antennas 208 of FIG. 2may be from vehicle 800 and processed as described with respect to atleast one of FIGS. 1-6 to provide information to vehicle 800 for itsautonomous operation, such as weather data, navigational data, roadcondition data, and/or may be used to provide a remote operator anability to control vehicle 800 remotely.

FIG. 8C is a block diagram illustrating an example system architecturefor autonomous vehicle 800 of FIG. 8A, according to at least oneembodiment. In at least one embodiment, each of components, features,and systems of vehicle 800 in FIG. 8C are illustrated as being connectedvia a bus 802. In at least one embodiment, bus 802 may include, withoutlimitation, a CAN data interface (alternatively referred to herein as a“CAN bus”). In at least one embodiment, a CAN may be a network insidevehicle 800 used to aid in control of various features and functionalityof vehicle 800, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. In at least one embodiment, bus 802may be configured to have dozens or even hundreds of nodes, each withits own unique identifier (e.g., a CAN ID). In at least one embodiment,bus 802 may be read to find steering wheel angle, ground speed, enginerevolutions per minute (“RPMs”), button positions, and/or other vehiclestatus indicators. In at least one embodiment, bus 802 may be a CAN busthat is ASIL B compliant.

In at least one embodiment, in addition to, or alternatively from CAN,FlexRay and/or Ethernet may be used. In at least one embodiment, theremay be any number of buses 802, which may include, without limitation,zero or more CAN busses, zero or more FlexRay busses, zero or moreEthernet busses, and/or zero or more other types of busses using adifferent protocol. In at least one embodiment, two or more busses 802may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 802 may be used for collisionavoidance functionality and a second bus 802 may be used for actuationcontrol. In at least one embodiment, each bus 802 may communicate withany of components of vehicle 800, and two or more busses 802 maycommunicate with same components. In at least one embodiment, each ofany number of system(s) on chip(s) (“SoC(s)”) 804, each of controller(s)836, and/or each computer within vehicle may have access to same inputdata (e.g., inputs from sensors of vehicle 800), and may be connected toa common bus, such CAN bus.

In at least one embodiment, vehicle 800 may include one or morecontroller(s) 836, such as those described herein with respect to FIG.8A. Controller(s) 836 may be used for a variety of functions. In atleast one embodiment, controller(s) 836 may be coupled to any of variousother components and systems of vehicle 800, and may be used for controlof vehicle 800, artificial intelligence of vehicle 800, infotainment forvehicle 800, and/or like.

In at least one embodiment, vehicle 800 may include any number of SoCs804. Each of SoCs 804 may include, without limitation, centralprocessing units (“CPU(s)”) 806, graphics processing units (“GPU(s)”)808, processor(s) 810, cache(s) 812, accelerator(s) 814, data store(s)816, and/or other components and features not illustrated. In at leastone embodiment, SoC(s) 804 may be used to control vehicle 800 in avariety of platforms and systems. For example, in at least oneembodiment, SoC(s) 804 may be combined in a system (e.g., system ofvehicle 800) with a High Definition (“HD”) map 822 which may obtain maprefreshes and/or updates via network interface 824 from one or moreservers (not shown in FIG. 8C).

In at least one embodiment, CPU(s) 806 may include a CPU cluster or CPUcomplex (alternatively referred to herein as a “CCPLEX”). In at leastone embodiment, CPU(s) 806 may include multiple cores and/or level two(“L2”) caches. For instance, in at least one embodiment, CPU(s) 806 mayinclude eight cores in a coherent multi-processor configuration. In atleast one embodiment, CPU(s) 806 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). Inat least one embodiment, CPU(s) 806 (e.g., CCPLEX) may be configured tosupport simultaneous cluster operation enabling any combination ofclusters of CPU(s) 806 to be active at any given time.

In at least one embodiment, one or more of CPU(s) 806 may implementpower management capabilities that include, without limitation, one ormore of following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when core is not actively executing instructions dueto execution of Wait for Interrupt (“WFI”)/Wait for Event (“WFE”)instructions; each core may be independently power-gated; each corecluster may be independently clock-gated when all cores are clock-gatedor power-gated; and/or each core cluster may be independentlypower-gated when all cores are power-gated. In at least one embodiment,CPU(s) 806 may further implement an enhanced algorithm for managingpower states, where allowed power states and expected wakeup times arespecified, and hardware/microcode determines best power state to enterfor core, cluster, and CCPLEX. In at least one embodiment, processingcores may support simplified power state entry sequences in softwarewith work offloaded to microcode.

In at least one embodiment, GPU(s) 808 may include an integrated GPU(alternatively referred to herein as an “iGPU”). In at least oneembodiment, GPU(s) 808 may be programmable and may be efficient forparallel workloads. In at least one embodiment, GPU(s) 808, in at leastone embodiment, may use an enhanced tensor instruction set. In onembodiment, GPU(s) 808 may include one or more streamingmicroprocessors, where each streaming microprocessor may include a levelone (“L1”) cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of streaming microprocessors may share an L2cache (e.g., an L2 cache with a 512 KB storage capacity). In at leastone embodiment, GPU(s) 808 may include at least eight streamingmicroprocessors. In at least one embodiment, GPU(s) 808 may use computeapplication programming interface(s) (API(s)). In at least oneembodiment, GPU(s) 808 may use one or more parallel computing platformsand/or programming models (e.g., NVIDIA's CUDA).

In at least one embodiment, one or more of GPU(s) 808 may bepower-optimized for best performance in automotive and embedded usecases. For example, in on embodiment, GPU(s) 808 could be fabricated ona Fin field-effect transistor (“FinFET”). In at least one embodiment,each streaming microprocessor may incorporate a number ofmixed-precision processing cores partitioned into multiple blocks. Forexample, and without limitation, 64 PF32 cores and 32 PF64 cores couldbe partitioned into four processing blocks. In at least one embodiment,each processing block could be allocated 16 FP32 cores, 8 FP64 cores, 16INT32 cores, two mixed-precision NVIDIA TENSOR COREs for deep learningmatrix arithmetic, a level zero (“L0”) instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In at leastone embodiment, streaming microprocessors may include independentparallel integer and floating-point data paths to provide for efficientexecution of workloads with a mix of computation and addressingcalculations. In at least one embodiment, streaming microprocessors mayinclude independent thread scheduling capability to enable finer-grainsynchronization and cooperation between parallel threads. In at leastone embodiment, streaming microprocessors may include a combined L1 datacache and shared memory unit in order to improve performance whilesimplifying programming.

In at least one embodiment, one or more of GPU(s) 808 may include a highbandwidth memory (“HBM) and/or a 16 GB HBM2 memory subsystem to provide,in some examples, about 900 GB/second peak memory bandwidth. In at leastone embodiment, in addition to, or alternatively from, HBM memory, asynchronous graphics random-access memory (“SGRAM”) may be used, such asa graphics double data rate type five synchronous random-access memory(“GDDR5”).

In at least one embodiment, GPU(s) 808 may include unified memorytechnology. In at least one embodiment, address translation services(“ATS”) support may be used to allow GPU(s) 808 to access CPU(s) 806page tables directly. In at least one embodiment, embodiment, whenGPU(s) 808 memory management unit (“MMU”) experiences a miss, an addresstranslation request may be transmitted to CPU(s) 806. In response,CPU(s) 806 may look in its page tables for virtual-to-physical mappingfor address and transmits translation back to GPU(s) 808, in at leastone embodiment. In at least one embodiment, unified memory technologymay allow a single unified virtual address space for memory of bothCPU(s) 806 and GPU(s) 808, thereby simplifying GPU(s) 808 programmingand porting of applications to GPU(s) 808.

In at least one embodiment, GPU(s) 808 may include any number of accesscounters that may keep track of frequency of access of GPU(s) 808 tomemory of other processors. In at least one embodiment, accesscounter(s) may help ensure that memory pages are moved to physicalmemory of processor that is accessing pages most frequently, therebyimproving efficiency for memory ranges shared between processors.

In at least one embodiment, one or more of SoC(s) 804 may include anynumber of cache(s) 812, including those described herein. For example,in at least one embodiment, cache(s) 812 could include a level three(“L3”) cache that is available to both CPU(s) 806 and GPU(s) 808 (e.g.,that is connected both CPU(s) 806 and GPU(s) 808). In at least oneembodiment, cache(s) 812 may include a write-back cache that may keeptrack of states of lines, such as by using a cache coherence protocol(e.g., MEI, MESI, MSI, etc.). In at least one embodiment, L3 cache mayinclude 4 MB or more, depending on embodiment, although smaller cachesizes may be used.

In at least one embodiment, one or more of SoC(s) 804 may include one ormore accelerator(s) 814 (e.g., hardware accelerators, softwareaccelerators, or a combination thereof). In at least one embodiment,SoC(s) 804 may include a hardware acceleration cluster that may includeoptimized hardware accelerators and/or large on-chip memory. In at leastone embodiment, large on-chip memory (e.g., 4 MB of SRAM), may enablehardware acceleration cluster to accelerate neural networks and othercalculations. In at least one embodiment, hardware acceleration clustermay be used to complement GPU(s) 808 and to off-load some of tasks ofGPU(s) 808 (e.g., to free up more cycles of GPU(s) 808 for performingother tasks). In at least one embodiment, accelerator(s) 814 could beused for targeted workloads (e.g., perception, convolutional neuralnetworks (“CNNs”), recurrent neural networks (“RNNs”), etc.) that arestable enough to be amenable to acceleration. In at least oneembodiment, a CNN may include a region-based or regional convolutionalneural networks (“RCNNs”) and Fast RCNNs (e.g., as used for objectdetection) or other type of CNN.

In at least one embodiment, accelerator(s) 814 (e.g., hardwareacceleration cluster) may include a deep learning accelerator(s) (“DLA).DLA(s) may include, without limitation, one or more Tensor processingunits (“TPUs) that may be configured to provide an additional tentrillion operations per second for deep learning applications andinferencing. In at least one embodiment, TPUs may be acceleratorsconfigured to, and optimized for, performing image processing functions(e.g., for CNNs, RCNNs, etc.). DLA(s) may further be optimized for aspecific set of neural network types and floating point operations, aswell as inferencing. In at least one embodiment, design of DLA(s) mayprovide more performance per millimeter than a typical general-purposeGPU, and typically vastly exceeds performance of a CPU. In at least oneembodiment, TPU(s) may perform several functions, including asingle-instance convolution function, supporting, for example, INT8,INT16, and FP16 data types for both features and weights, as well aspost-processor functions. In at least one embodiment, DLA(s) may quicklyand efficiently execute neural networks, especially CNNs, on processedor unprocessed data for any of a variety of functions, including, forexample and without limitation: a CNN for object identification anddetection using data from camera sensors; a CNN for distance estimationusing data from camera sensors; a CNN for emergency vehicle detectionand identification and detection using data from microphones 896; a CNNfor facial recognition and vehicle owner identification using data fromcamera sensors; and/or a CNN for security and/or safety related events.

In at least one embodiment, DLA(s) may perform any function of GPU(s)808, and by using an inference accelerator, for example, a designer maytarget either DLA(s) or GPU(s) 808 for any function. For example, in atleast one embodiment, designer may focus processing of CNNs and floatingpoint operations on DLA(s) and leave other functions to GPU(s) 808and/or other accelerator(s) 814.

In at least one embodiment, accelerator(s) 814 (e.g., hardwareacceleration cluster) may include a programmable vision accelerator(s)(“PVA”), which may alternatively be referred to herein as a computervision accelerator. In at least one embodiment, PVA(s) may be designedand configured to accelerate computer vision algorithms for advanceddriver assistance system (“ADAS”) 838, autonomous driving, augmentedreality (“AR”) applications, and/or virtual reality (“VR”) applications.PVA(s) may provide a balance between performance and flexibility. Forexample, in at least one embodiment, each PVA(s) may include, forexample and without limitation, any number of reduced instruction setcomputer (“RISC”) cores, direct memory access (“DMA”), and/or any numberof vector processors.

In at least one embodiment, RISC cores may interact with image sensors(e.g., image sensors of any of cameras described herein), image signalprocessor(s), and/or like. In at least one embodiment, each of RISCcores may include any amount of memory. In at least one embodiment, RISCcores may use any of a number of protocols, depending on embodiment. Inat least one embodiment, RISC cores may execute a real-time operatingsystem (“RTOS”). In at least one embodiment, RISC cores may beimplemented using one or more integrated circuit devices, applicationspecific integrated circuits (“ASICs”), and/or memory devices. Forexample, in at least one embodiment, RISC cores could include aninstruction cache and/or a tightly coupled RAM.

In at least one embodiment, DMA may enable components of PVA(s) toaccess system memory independently of CPU(s) 806. In at least oneembodiment, DMA may support any number of features used to provideoptimization to PVA including, but not limited to, supportingmulti-dimensional addressing and/or circular addressing. In at least oneembodiment, DMA may support up to six or more dimensions of addressing,which may include, without limitation, block width, block height, blockdepth, horizontal block stepping, vertical block stepping, and/or depthstepping.

In at least one embodiment, vector processors may be programmableprocessors that may be designed to efficiently and flexibly executeprogramming for computer vision algorithms and provide signal processingcapabilities. In at least one embodiment, PVA may include a PVA core andtwo vector processing subsystem partitions. In at least one embodiment,PVA core may include a processor subsystem, DMA engine(s) (e.g., two DMAengines), and/or other peripherals. In at least one embodiment, vectorprocessing subsystem may operate as primary processing engine of PVA,and may include a vector processing unit (“VPU”), an instruction cache,and/or vector memory (e.g., “VMEM”). In at least one embodiment, VPUcore may include a digital signal processor such as, for example, asingle instruction, multiple data (“SIMD”), very long instruction word(“VLIW”) digital signal processor. In at least one embodiment, acombination of SIMD and VLIW may enhance throughput and speed.

In at least one embodiment, each of vector processors may include aninstruction cache and may be coupled to dedicated memory. As a result,in at least one embodiment, each of vector processors may be configuredto execute independently of other vector processors. In at least oneembodiment, vector processors that are included in a particular PVA maybe configured to employ data parallelism. For instance, in at least oneembodiment, plurality of vector processors included in a single PVA mayexecute same computer vision algorithm, but on different regions of animage. In at least one embodiment, vector processors included in aparticular PVA may simultaneously execute different computer visionalgorithms, on same image, or even execute different algorithms onsequential images or portions of an image. In at least one embodiment,among other things, any number of PVAs may be included in hardwareacceleration cluster and any number of vector processors may be includedin each of PVAs. In at least one embodiment, PVA(s) may includeadditional error correcting code (“ECC”) memory, to enhance overallsystem safety.

In at least one embodiment, accelerator(s) 814 (e.g., hardwareacceleration cluster) may include a computer vision network on-chip andstatic random-access memory (“SRAM”), for providing a high-bandwidth,low latency SRAM for accelerator(s) 814. In at least one embodiment,on-chip memory may include at least 4 MB SRAM, consisting of, forexample and without limitation, eight field-configurable memory blocks,that may be accessible by both PVA and DLA. In at least one embodiment,each pair of memory blocks may include an advanced peripheral bus(“APB”) interface, configuration circuitry, a controller, and amultiplexer. In at least one embodiment, any type of memory may be used.In at least one embodiment, PVA and DLA may access memory via a backbonethat provides PVA and DLA with high-speed access to memory. In at leastone embodiment, backbone may include a computer vision network on-chipthat interconnects PVA and DLA to memory (e.g., using APB).

In at least one embodiment, computer vision network on-chip may includean interface that determines, before transmission of any controlsignal/address/data, that both PVA and DLA provide ready and validsignals. In at least one embodiment, an interface may provide forseparate phases and separate channels for transmitting controlsignals/addresses/data, as well as burst-type communications forcontinuous data transfer. In at least one embodiment, an interface maycomply with International Organization for Standardization (“ISO”) 26262or International Electrotechnical Commission (“IEC”) 61508 standards,although other standards and protocols may be used.

In at least one embodiment, one or more of SoC(s) 804 may include areal-time ray-tracing hardware accelerator. In at least one embodiment,real-time ray-tracing hardware accelerator may be used to quickly andefficiently determine positions and extents of objects (e.g., within aworld model), to generate real-time visualization simulations, for RADARsignal interpretation, for sound propagation synthesis and/or analysis,for simulation of SONAR systems, for general wave propagationsimulation, for comparison to LIDAR data for purposes of localizationand/or other functions, and/or for other uses.

In at least one embodiment, accelerator(s) 814 (e.g., hardwareaccelerator cluster) have a wide array of uses for autonomous driving.In at least one embodiment, PVA may be a programmable vision acceleratorthat may be used for key processing stages in ADAS and autonomousvehicles. In at least one embodiment, PVA's capabilities are a goodmatch for algorithmic domains needing predictable processing, at lowpower and low latency. In other words, PVA performs well on semi-denseor dense regular computation, even on small data sets, which needpredictable run-times with low latency and low power. In at least oneembodiment, autonomous vehicles, such as vehicle 800, PVAs are designedto run classic computer vision algorithms, as they are efficient atobject detection and operating on integer math.

For example, according to at least one embodiment of technology, PVA isused to perform computer stereo vision. In at least one embodiment,semi-global matching-based algorithm may be used in some examples,although this is not intended to be limiting. In at least oneembodiment, applications for Level 3-5 autonomous driving use motionestimation/stereo matching on-the-fly (e.g., structure from motion,pedestrian recognition, lane detection, etc.). In at least oneembodiment, PVA may perform computer stereo vision function on inputsfrom two monocular cameras.

In at least one embodiment, PVA may be used to perform dense opticalflow. For example, in at least one embodiment, PVA could process rawRADAR data (e.g., using a 4D Fast Fourier Transform) to provideprocessed RADAR data. In at least one embodiment, PVA is used for timeof flight depth processing, by processing raw time of flight data toprovide processed time of flight data, for example.

In at least one embodiment, DLA may be used to run any type of networkto enhance control and driving safety, including for example and withoutlimitation, a neural network that outputs a measure of confidence foreach object detection. In at least one embodiment, confidence may berepresented or interpreted as a probability, or as providing a relative“weight” of each detection compared to other detections. In at least oneembodiment, confidence enables a system to make further decisionsregarding which detections should be considered as true positivedetections rather than false positive detections. For example, In atleast one embodiment, a system may set a threshold value for confidenceand consider only detections exceeding threshold value as true positivedetections. In an embodiment in which an automatic emergency braking(“AEB”) system is used, false positive detections would cause vehicle toautomatically perform emergency braking, which is obviously undesirable.In at least one embodiment, highly confident detections may beconsidered as triggers for AEB In at least one embodiment, DLA may run aneural network for regressing confidence value. In at least oneembodiment, neural network may take as its input at least some subset ofparameters, such as bounding box dimensions, ground plane estimateobtained (e.g. from another subsystem), output from IMU sensor(s) 866that correlates with vehicle 800 orientation, distance, 3D locationestimates of object obtained from neural network and/or other sensors(e.g., LIDAR sensor(s) 864 or RADAR sensor(s) 860), among others.

In at least one embodiment, one or more of SoC(s) 804 may include datastore(s) 816 (e.g., memory). In at least one embodiment, data store(s)816 may be on-chip memory of SoC(s) 804, which may store neural networksto be executed on GPU(s) 808 and/or DLA. In at least one embodiment,data store(s) 816 may be large enough in capacity to store multipleinstances of neural networks for redundancy and safety. In at least oneembodiment, data store(s) 812 may comprise L2 or L3 cache(s).

In at least one embodiment, one or more of SoC(s) 804 may include anynumber of processor(s) 810 (e.g., embedded processors). Processor(s) 810may include a boot and power management processor that may be adedicated processor and subsystem to handle boot power and managementfunctions and related security enforcement. In at least one embodiment,boot and power management processor may be a part of SoC(s) 804 bootsequence and may provide runtime power management services. In at leastone embodiment, boot power and management processor may provide clockand voltage programming, assistance in system low power statetransitions, management of SoC(s) 804 thermals and temperature sensors,and/or management of SoC(s) 804 power states. In at least oneembodiment, each temperature sensor may be implemented as aring-oscillator whose output frequency is proportional to temperature,and SoC(s) 804 may use ring-oscillators to detect temperatures of CPU(s)806, GPU(s) 808, and/or accelerator(s) 814. In at least one embodiment,if temperatures are determined to exceed a threshold, then boot andpower management processor may enter a temperature fault routine and putSoC(s) 804 into a lower power state and/or put vehicle 800 into achauffeur to safe stop mode (e.g., bring vehicle 800 to a safe stop).

In at least one embodiment, processor(s) 810 may further include a setof embedded processors that may serve as an audio processing engine. Inat least one embodiment, audio processing engine may be an audiosubsystem that enables full hardware support for multi-channel audioover multiple interfaces, and a broad and flexible range of audio I/Ointerfaces. In at least one embodiment, audio processing engine is adedicated processor core with a digital signal processor with dedicatedRAM.

In at least one embodiment, processor(s) 810 may further include analways on processor engine that may provide necessary hardware featuresto support low power sensor management and wake use cases. In at leastone embodiment, always on processor engine may include, withoutlimitation, a processor core, a tightly coupled RAM, supportingperipherals (e.g., timers and interrupt controllers), various I/Ocontroller peripherals, and routing logic.

In at least one embodiment, processor(s) 810 may further include asafety cluster engine that includes, without limitation, a dedicatedprocessor subsystem to handle safety management for automotiveapplications. In at least one embodiment, safety cluster engine mayinclude, without limitation, two or more processor cores, a tightlycoupled RAM, support peripherals (e.g., timers, an interrupt controller,etc.), and/or routing logic. In a safety mode, two or more cores mayoperate, in at least one embodiment, in a lockstep mode and function asa single core with comparison logic to detect any differences betweentheir operations. In at least one embodiment, processor(s) 810 mayfurther include a real-time camera engine that may include, withoutlimitation, a dedicated processor subsystem for handling real-timecamera management. In at least one embodiment, processor(s) 810 mayfurther include a high-dynamic range signal processor that may include,without limitation, an image signal processor that is a hardware enginethat is part of camera processing pipeline.

In at least one embodiment, processor(s) 810 may include a video imagecompositor that may be a processing block (e.g., implemented on amicroprocessor) that implements video post-processing functions neededby a video playback application to produce final image for playerwindow. In at least one embodiment, video image compositor may performlens distortion correction on wide-view camera(s) 870, surroundcamera(s) 874, and/or on in-cabin monitoring camera sensor(s). In atleast one embodiment, in-cabin monitoring camera sensor(s) arepreferably monitored by a neural network running on another instance ofSoC 804, configured to identify in cabin events and respond accordingly.In at least one embodiment, an in-cabin system may perform, withoutlimitation, lip reading to activate cellular service and place a phonecall, dictate emails, change vehicle's destination, activate or changevehicle's infotainment system and settings, or provide voice-activatedweb surfing. In at least one embodiment, certain functions are availableto driver when vehicle is operating in an autonomous mode and aredisabled otherwise.

In at least one embodiment, video image compositor may include enhancedtemporal noise reduction for both spatial and temporal noise reduction.For example, in at least one embodiment, where motion occurs in a video,noise reduction weights spatial information appropriately, decreasingweight of information provided by adjacent frames. In at least oneembodiment, where an image or portion of an image does not includemotion, temporal noise reduction performed by video image compositor mayuse information from previous image to reduce noise in current image.

In at least one embodiment, video image compositor may also beconfigured to perform stereo rectification on input stereo lens frames.In at least one embodiment, video image compositor may further be usedfor user interface composition when operating system desktop is in use,and GPU(s) 808 are not required to continuously render new surfaces. Inat least one embodiment, when GPU(s) 808 are powered on and active doing3D rendering, video image compositor may be used to offload GPU(s) 808to improve performance and responsiveness.

In at least one embodiment, one or more of SoC(s) 804 may furtherinclude a mobile industry processor interface (“MIPI”) camera serialinterface for receiving video and input from cameras, a high-speedinterface, and/or a video input block that may be used for camera andrelated pixel input functions. In at least one embodiment, one or moreof SoC(s) 804 may further include an input/output controller(s) that maybe controlled by software and may be used for receiving I/O signals thatare uncommitted to a specific role.

In at least one embodiment, one or more of SoC(s) 804 may furtherinclude a broad range of peripheral interfaces to enable communicationwith peripherals, audio encoders/decoders (“codecs”), power management,and/or other devices. SoC(s) 804 may be used to process data fromcameras (e.g., connected over Gigabit Multimedia Serial Link andEthernet), sensors (e.g., LIDAR sensor(s) 864, RADAR sensor(s) 860, etc.that may be connected over Ethernet), data from bus 802 (e.g., speed ofvehicle 800, steering wheel position, etc.), data from GNSS sensor(s)858 (e.g., connected over Ethernet or CAN bus), etc. In at least oneembodiment, one or more of SoC(s) 804 may further include dedicatedhigh-performance mass storage controllers that may include their own DMAengines, and that may be used to free CPU(s) 806 from routine datamanagement tasks.

In at least one embodiment, SoC(s) 804 may be an end-to-end platformwith a flexible architecture that spans automation levels 3-5, therebyproviding a comprehensive functional safety architecture that leveragesand makes efficient use of computer vision and ADAS techniques fordiversity and redundancy, provides a platform for a flexible, reliabledriving software stack, along with deep learning tools. In at least oneembodiment, SoC(s) 804 may be faster, more reliable, and even moreenergy-efficient and space-efficient than conventional systems. Forexample, in at least one embodiment, accelerator(s) 814, when combinedwith CPU(s) 806, GPU(s) 808, and data store(s) 816, may provide for afast, efficient platform for level 3-5 autonomous vehicles.

In at least one embodiment, computer vision algorithms may be executedon CPUs, which may be configured using high-level programming language,such as C programming language, to execute a wide variety of processingalgorithms across a wide variety of visual data. However, in at leastone embodiment, CPUs are oftentimes unable to meet performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In at least oneembodiment, many CPUs are unable to execute complex object detectionalgorithms in real-time, which is used in in-vehicle ADAS applicationsand in practical Level 3-5 autonomous vehicles.

Embodiments described herein allow for multiple neural networks to beperformed simultaneously and/or sequentially, and for results to becombined together to enable Level 3-5 autonomous driving functionality.For example, in at least one embodiment, a CNN executing on DLA ordiscrete GPU (e.g., GPU(s) 820) may include text and word recognition,allowing supercomputer to read and understand traffic signs, includingsigns for which neural network has not been specifically trained. In atleast one embodiment, DLA may further include a neural network that isable to identify, interpret, and provide semantic understanding of sign,and to pass that semantic understanding to path planning modules runningon CPU Complex.

In at least one embodiment, multiple neural networks may be runsimultaneously, as for Level 3, 4, or 5 driving. For example, in atleast one embodiment, a warning sign consisting of “Caution: flashinglights indicate icy conditions,” along with an electric light, may beindependently or collectively interpreted by several neural networks. Inat least one embodiment, sign itself may be identified as a traffic signby a first deployed neural network (e.g., a neural network that has beentrained), text “flashing lights indicate icy conditions” may beinterpreted by a second deployed neural network, which informs vehicle'spath planning software (preferably executing on CPU Complex) that whenflashing lights are detected, icy conditions exist. In at least oneembodiment, flashing light may be identified by operating a thirddeployed neural network over multiple frames, informing vehicle'spath-planning software of presence (or absence) of flashing lights. Inat least one embodiment, all three neural networks may runsimultaneously, such as within DLA and/or on GPU(s) 808.

In at least one embodiment, a CNN for facial recognition and vehicleowner identification may use data from camera sensors to identifypresence of an authorized driver and/or owner of vehicle 800. In atleast one embodiment, an always on sensor processing engine may be usedto unlock vehicle when owner approaches driver door and turn on lights,and, in security mode, to disable vehicle when owner leaves vehicle. Inthis way, SoC(s) 804 provide for security against theft and/orcarjacking.

In at least one embodiment, a CNN for emergency vehicle detection andidentification may use data from microphones 896 to detect and identifyemergency vehicle sirens. In at least one embodiment, SoC(s) 804 use CNNfor classifying environmental and urban sounds, as well as classifyingvisual data. In at least one embodiment, CNN running on DLA is trainedto identify relative closing speed of emergency vehicle (e.g., by usingDoppler effect). In at least one embodiment, CNN may also be trained toidentify emergency vehicles specific to local area in which vehicle isoperating, as identified by GNSS sensor(s) 858. In at least oneembodiment, when operating in Europe, CNN will seek to detect Europeansirens, and when in United States CNN will seek to identify only NorthAmerican sirens. In at least one embodiment, once an emergency vehicleis detected, a control program may be used to execute an emergencyvehicle safety routine, slowing vehicle, pulling over to side of road,parking vehicle, and/or idling vehicle, with assistance of ultrasonicsensor(s) 862, until emergency vehicle(s) passes.

In at least one embodiment, vehicle 800 may include CPU(s) 818 (e.g.,discrete CPU(s), or dCPU(s)), that may be coupled to SoC(s) 804 via ahigh-speed interconnect (e.g., PCIe). In at least one embodiment, CPU(s)818 may include an X86 processor, for example. CPU(s) 818 may be used toperform any of a variety of functions, including arbitrating potentiallyinconsistent results between ADAS sensors and SoC(s) 804, and/ormonitoring status and health of controller(s) 836 and/or an infotainmentsystem on a chip (“infotainment SoC”) 830, for example.

In at least one embodiment, vehicle 800 may include GPU(s) 820 (e.g.,discrete GPU(s), or dGPU(s)), that may be coupled to SoC(s) 804 via ahigh-speed interconnect (e.g., NVIDIA's NVLINK). In at least oneembodiment, GPU(s) 820 may provide additional artificial intelligencefunctionality, such as by executing redundant and/or different neuralnetworks, and may be used to train and/or update neural networks basedat least in part on input (e.g., sensor data) from sensors of vehicle800.

In at least one embodiment, vehicle 800 may further include networkinterface 824 which may include, without limitation, wireless antenna(s)826 (e.g., one or more wireless antennas 826 for different communicationprotocols, such as a cellular antenna, a Bluetooth antenna, etc.). In atleast one embodiment, network interface 824 may be used to enablewireless connectivity over Internet with cloud (e.g., with server(s)and/or other network devices), with other vehicles, and/or withcomputing devices (e.g., client devices of passengers). In at least oneembodiment, to communicate with other vehicles, a direct link may beestablished between vehicle 800 and other vehicle and/or an indirectlink may be established (e.g., across networks and over Internet). In atleast one embodiment, direct links may be provided using avehicle-to-vehicle communication link. vehicle-to-vehicle communicationlink may provide vehicle 800 information about vehicles in proximity tovehicle 800 (e.g., vehicles in front of, on side of, and/or behindvehicle 800). In at least one embodiment, aforementioned functionalitymay be part of a cooperative adaptive cruise control functionality ofvehicle 800.

In at least one embodiment, network interface 824 may include an SoCthat provides modulation and demodulation functionality and enablescontroller(s) 836 to communicate over wireless networks. In at least oneembodiment, network interface 824 may include a radio frequencyfront-end for up-conversion from baseband to radio frequency, and downconversion from radio frequency to baseband. In at least one embodiment,frequency conversions may be performed in any technically feasiblefashion. For example, frequency conversions could be performed throughwell-known processes, and/or using super-heterodyne processes. In atleast one embodiment, radio frequency front end functionality may beprovided by a separate chip. In at least one embodiment, networkinterface may include wireless functionality for communicating over LTE,WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave,ZigBee, LoRaWAN, and/or other wireless protocols.

In at least one embodiment, vehicle 800 may further include datastore(s) 828 which may include, without limitation, off-chip (e.g., offSoC(s) 804) storage. In at least one embodiment, data store(s) 828 mayinclude, without limitation, one or more storage elements including RAM,SRAM, dynamic random-access memory (“DRAM”), video random-access memory(“VRAM”), Flash, hard disks, and/or other components and/or devices thatmay store at least one bit of data.

In at least one embodiment, vehicle 800 may further include GNSSsensor(s) 858 (e.g., GPS and/or assisted GPS sensors), to assist inmapping, perception, occupancy grid generation, and/or path planningfunctions. In at least one embodiment, any number of GNSS sensor(s) 858may be used, including, for example and without limitation, a GPS usinga USB connector with an Ethernet to Serial (e.g., RS-232) bridge.

In at least one embodiment, vehicle 800 may further include RADARsensor(s) 860. RADAR sensor(s) 860 may be used by vehicle 800 forlong-range vehicle detection, even in darkness and/or severe weatherconditions. In at least one embodiment, RADAR functional safety levelsmay be ASIL B. RADAR sensor(s) 860 may use CAN and/or bus 802 (e.g., totransmit data generated by RADAR sensor(s) 860) for control and toaccess object tracking data, with access to Ethernet to access raw datain some examples. In at least one embodiment, wide variety of RADARsensor types may be used. For example, and without limitation, RADARsensor(s) 860 may be suitable for front, rear, and side RADAR use. In atleast one embodiment, one or more of RADAR sensors(s) 860 are PulseDoppler RADAR sensor(s).

In at least one embodiment, RADAR sensor(s) 860 may include differentconfigurations, such as long-range with narrow field of view,short-range with wide field of view, short-range side coverage, etc. Inat least one embodiment, long-range RADAR may be used for adaptivecruise control functionality. In at least one embodiment, long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. In at least oneembodiment, RADAR sensor(s) 860 may help in distinguishing betweenstatic and moving objects, and may be used by ADAS system 838 foremergency brake assist and forward collision warning. Sensors 860(s)included in a long-range RADAR system may include, without limitation,monostatic multimodal RADAR with multiple (e.g., six or more) fixedRADAR antennae and a high-speed CAN and FlexRay interface. In at leastone embodiment, with six antennae, central four antennae may create afocused beam pattern, designed to record vehicle's 800 surroundings athigher speeds with minimal interference from traffic in adjacent lanes.In at least one embodiment, other two antennae may expand field of view,making it possible to quickly detect vehicles entering or leavingvehicle's 800 lane.

In at least one embodiment, mid-range RADAR systems may include, as anexample, a range of up to 160 m (front) or 80 m (rear), and a field ofview of up to 42 degrees (front) or 150 degrees (rear). In at least oneembodiment, short-range RADAR systems may include, without limitation,any number of RADAR sensor(s) 860 designed to be installed at both endsof rear bumper. When installed at both ends of rear bumper, in at leastone embodiment, a RADAR sensor system may create two beams thatconstantly monitor blind spot in rear and next to vehicle. In at leastone embodiment, short-range RADAR systems may be used in ADAS system 838for blind spot detection and/or lane change assist.

In at least one embodiment, vehicle 800 may further include ultrasonicsensor(s) 862. ultrasonic sensor(s) 862, which may be positioned atfront, back, and/or sides of vehicle 800, may be used for park assistand/or to create and update an occupancy grid. In at least oneembodiment, a wide variety of ultrasonic sensor(s) 862 may be used, anddifferent ultrasonic sensor(s) 862 may be used for different ranges ofdetection (e.g., 2.5 m, 4 m). In at least one embodiment, ultrasonicsensor(s) 862 may operate at functional safety levels of ASIL B.

In at least one embodiment, vehicle 800 may include LIDAR sensor(s) 864.LIDAR sensor(s) 864 may be used for object and pedestrian detection,emergency braking, collision avoidance, and/or other functions. In atleast one embodiment, LIDAR sensor(s) 864 may be functional safety levelASIL B. In at least one embodiment, vehicle 800 may include multipleLIDAR sensors 864 (e.g., two, four, six, etc.) that may use Ethernet(e.g., to provide data to a Gigabit Ethernet switch).

In at least one embodiment, LIDAR sensor(s) 864 may be capable ofproviding a list of objects and their distances for a 360-degree fieldof view. In at least one embodiment, commercially available LIDARsensor(s) 864 may have an advertised range of approximately 100 m, withan accuracy of 2 cm-3 cm, and with support for a 100 Mbps Ethernetconnection, for example. In at least one embodiment, one or morenon-protruding LIDAR sensors 864 may be used. In such an embodiment,LIDAR sensor(s) 864 may be implemented as a small device that may beembedded into front, rear, sides, and/or corners of vehicle 800. In atleast one embodiment, LIDAR sensor(s) 864, in such an embodiment, mayprovide up to a 120-degree horizontal and 35-degree verticalfield-of-view, with a 200 m range even for low-reflectivity objects. Inat least one embodiment, front-mounted LIDAR sensor(s) 864 may beconfigured for a horizontal field of view between 45 degrees and 135degrees.

In at least one embodiment, LIDAR technologies, such as 3D flash LIDAR,may also be used. 3D Flash LIDAR uses a flash of a laser as atransmission source, to illuminate surroundings of vehicle 800 up toapproximately 200 m. In at least one embodiment, a flash LIDAR unitincludes, without limitation, a receptor, which records laser pulsetransit time and reflected light on each pixel, which in turncorresponds to range from vehicle 800 to objects. In at least oneembodiment, flash LIDAR may allow for highly accurate anddistortion-free images of surroundings to be generated with every laserflash. In at least one embodiment, four flash LIDAR sensors may bedeployed, one at each side of vehicle 800. In at least one embodiment,3D flash LIDAR systems include, without limitation, a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). In at least one embodiment, flash LIDARdevice may use a 5 nanosecond class I (eye-safe) laser pulse per frameand may capture reflected laser light in form of 3D range point cloudsand co-registered intensity data.

In at least one embodiment, vehicle may further include IMU sensor(s)866. In at least one embodiment, IMU sensor(s) 866 may be located at acenter of rear axle of vehicle 800, in at least one embodiment. In atleast one embodiment, IMU sensor(s) 866 may include, for example andwithout limitation, accelerometer(s), magnetometer(s), gyroscope(s),magnetic compass(es), and/or other sensor types. In at least oneembodiment, such as in six-axis applications, IMU sensor(s) 866 mayinclude, without limitation, accelerometers and gyroscopes. In at leastone embodiment, such as in nine-axis applications, IMU sensor(s) 866 mayinclude, without limitation, accelerometers, gyroscopes, andmagnetometers.

In at least one embodiment, IMU sensor(s) 866 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(“GPS/INS”) that combines micro-electro-mechanical systems (“MEMS”)inertial sensors, a high-sensitivity GPS receiver, and advanced Kalmanfiltering algorithms to provide estimates of position, velocity, andattitude. In at least one embodiment, IMU sensor(s) 866 may enablevehicle 800 to estimate heading without requiring input from a magneticsensor by directly observing and correlating changes in velocity fromGPS to IMU sensor(s) 866. In at least one embodiment, IMU sensor(s) 866and GNSS sensor(s) 858 may be combined in a single integrated unit.

In at least one embodiment, vehicle 800 may include microphone(s) 896placed in and/or around vehicle 800. In at least one embodiment,microphone(s) 896 may be used for emergency vehicle detection andidentification, among other things.

In at least one embodiment, vehicle 800 may further include any numberof camera types, including stereo camera(s) 868, wide-view camera(s)870, infrared camera(s) 872, surround camera(s) 874, long-rangecamera(s) 898, mid-range camera(s) 876, and/or other camera types. In atleast one embodiment, cameras may be used to capture image data aroundan entire periphery of vehicle 800. In at least one embodiment, types ofcameras used depends vehicle 800. In at least one embodiment, anycombination of camera types may be used to provide necessary coveragearound vehicle 800. In at least one embodiment, number of cameras maydiffer depending on embodiment. For example, in at least one embodiment,vehicle 800 could include six cameras, seven cameras, ten cameras,twelve cameras, or another number of cameras. cameras may support, as anexample and without limitation, Gigabit Multimedia Serial Link (“GMSL”)and/or Gigabit Ethernet. In at least one embodiment, each of camera(s)is described with more detail previously herein with respect to FIG. 8Aand FIG. 8B.

In at least one embodiment, vehicle 800 may further include vibrationsensor(s) 842. vibration sensor(s) 842 may measure vibrations ofcomponents of vehicle 800, such as axle(s). For example, in at least oneembodiment, changes in vibrations may indicate a change in roadsurfaces. In at least one embodiment, when two or more vibration sensors842 are used, differences between vibrations may be used to determinefriction or slippage of road surface (e.g., when difference in vibrationis between a power-driven axle and a freely rotating axle).

In at least one embodiment, vehicle 800 may include ADAS system 838.ADAS system 838 may include, without limitation, an SoC, in someexamples. In at least one embodiment, ADAS system 838 may include,without limitation, any number and combination of anautonomous/adaptive/automatic cruise control (“ACC”) system, acooperative adaptive cruise control (“CACC”) system, a forward crashwarning (“FCW”) system, an automatic emergency braking (“AEB”) system, alane departure warning (“LDW)” system, a lane keep assist (“LKA”)system, a blind spot warning (“BSW”) system, a rear cross-trafficwarning (“RCTW”) system, a collision warning (“CW”) system, a lanecentering (“LC”) system, and/or other systems, features, and/orfunctionality.

In at least one embodiment, ACC system may use RADAR sensor(s) 860,LIDAR sensor(s) 864, and/or any number of camera(s). In at least oneembodiment, ACC system may include a longitudinal ACC system and/or alateral ACC system. In at least one embodiment, longitudinal ACC systemmonitors and controls distance to vehicle immediately ahead of vehicle800 and automatically adjust speed of vehicle 800 to maintain a safedistance from vehicles ahead. In at least one embodiment, lateral ACCsystem performs distance keeping, and advises vehicle 800 to changelanes when necessary. In at least one embodiment, lateral ACC is relatedto other ADAS applications such as LC and CW.

In at least one embodiment, CACC system uses information from othervehicles that may be received via network interface 824 and/or wirelessantenna(s) 826 from other vehicles via a wireless link, or indirectly,over a network connection (e.g., over Internet). In at least oneembodiment, direct links may be provided by a vehicle-to-vehicle (“V2V”)communication link, while indirect links may be provided by aninfrastructure-to-vehicle (“I2V”) communication link. In general, V2Vcommunication concept provides information about immediately precedingvehicles (e.g., vehicles immediately ahead of and in same lane asvehicle 800), while I2V communication concept provides information abouttraffic further ahead. In at least one embodiment, CACC system mayinclude either or both I2V and V2V information sources. In at least oneembodiment, given information of vehicles ahead of vehicle 800, CACCsystem may be more reliable and it has potential to improve traffic flowsmoothness and reduce congestion on road.

In at least one embodiment, FCW system is designed to alert driver to ahazard, so that driver may take corrective action. In at least oneembodiment, FCW system uses a front-facing camera and/or RADAR sensor(s)860, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component. In at least one embodiment, FCW system mayprovide a warning, such as in form of a sound, visual warning, vibrationand/or a quick brake pulse.

In at least one embodiment, AEB system detects an impending forwardcollision with another vehicle or other object, and may automaticallyapply brakes if driver does not take corrective action within aspecified time or distance parameter. In at least one embodiment, AEBsystem may use front-facing camera(s) and/or RADAR sensor(s) 860,coupled to a dedicated processor, DSP, FPGA, and/or ASIC. In at leastone embodiment, when AEB system detects a hazard, AEB system typicallyfirst alerts driver to take corrective action to avoid collision and, ifdriver does not take corrective action, AEB system may automaticallyapply brakes in an effort to prevent, or at least mitigate, impact ofpredicted collision. In at least one embodiment, AEB system, may includetechniques such as dynamic brake support and/or crash imminent braking.

In at least one embodiment, LDW system provides visual, audible, and/ortactile warnings, such as steering wheel or seat vibrations, to alertdriver when vehicle 800 crosses lane markings. In at least oneembodiment, LDW system does not activate when driver indicates anintentional lane departure, by activating a turn signal. In at least oneembodiment, LDW system may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent. In at least one embodiment, LKA system is a variation of LDWsystem. LKA system provides steering input or braking to correct vehicle800 if vehicle 800 starts to exit lane.

In at least one embodiment, BSW system detects and warns driver ofvehicles in an automobile's blind spot. In at least one embodiment, BSWsystem may provide a visual, audible, and/or tactile alert to indicatethat merging or changing lanes is unsafe. In at least one embodiment,BSW system may provide an additional warning when driver uses a turnsignal. In at least one embodiment, BSW system may use rear-side facingcamera(s) and/or RADAR sensor(s) 860, coupled to a dedicated processor,DSP, FPGA, and/or ASIC, that is electrically coupled to driver feedback,such as a display, speaker, and/or vibrating component.

In at least one embodiment, RCTW system may provide visual, audible,and/or tactile notification when an object is detected outsiderear-camera range when vehicle 800 is backing up. In at least oneembodiment, RCTW system includes AEB system to ensure that vehiclebrakes are applied to avoid a crash. In at least one embodiment, RCTWsystem may use one or more rear-facing RADAR sensor(s) 860, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

In at least one embodiment, conventional ADAS systems may be prone tofalse positive results which may be annoying and distracting to adriver, but typically are not catastrophic, because conventional ADASsystems alert driver and allow driver to decide whether a safetycondition truly exists and act accordingly. In at least one embodiment,vehicle 800 itself decides, in case of conflicting results, whether toheed result from a primary computer or a secondary computer (e.g., firstcontroller 836 or second controller 836). For example, in at least oneembodiment, ADAS system 838 may be a backup and/or secondary computerfor providing perception information to a backup computer rationalitymodule. In at least one embodiment, backup computer rationality monitormay run a redundant diverse software on hardware components to detectfaults in perception and dynamic driving tasks. In at least oneembodiment, outputs from ADAS system 838 may be provided to asupervisory MCU. In at least one embodiment, if outputs from primarycomputer and secondary computer conflict, supervisory MCU determines howto reconcile conflict to ensure safe operation.

In at least one embodiment, primary computer may be configured toprovide supervisory MCU with a confidence score, indicating primarycomputer's confidence in chosen result. In at least one embodiment, ifconfidence score exceeds a threshold, supervisory MCU may follow primarycomputer's direction, regardless of whether secondary computer providesa conflicting or inconsistent result. In at least one embodiment, whereconfidence score does not meet threshold, and where primary andsecondary computer indicate different results (e.g., a conflict),supervisory MCU may arbitrate between computers to determine appropriateoutcome.

In at least one embodiment, supervisory MCU may be configured to run aneural network(s) that is trained and configured to determine, based atleast in part on outputs from primary computer and secondary computer,conditions under which secondary computer provides false alarms. In atleast one embodiment, neural network(s) in supervisory MCU may learnwhen secondary computer's output may be trusted, and when it cannot. Forexample, in at least one embodiment, when secondary computer is aRADAR-based FCW system, a neural network(s) in supervisory MCU may learnwhen FCW system is identifying metallic objects that are not, in fact,hazards, such as a drainage grate or manhole cover that triggers analarm. In at least one embodiment, when secondary computer is acamera-based LDW system, a neural network in supervisory MCU may learnto override LDW when bicyclists or pedestrians are present and a lanedeparture is, in fact, safest maneuver. In at least one embodiment,supervisory MCU may include at least one of a DLA or GPU suitable forrunning neural network(s) with associated memory. In at least oneembodiment, supervisory MCU may comprise and/or be included as acomponent of SoC(s) 804.

In at least one embodiment, ADAS system 838 may include a secondarycomputer that performs ADAS functionality using traditional rules ofcomputer vision. In at least one embodiment, secondary computer may useclassic computer vision rules (if-then), and presence of a neuralnetwork(s) in supervisory MCU may improve reliability, safety andperformance. For example, in at least one embodiment, diverseimplementation and intentional non-identity makes overall system morefault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, in at least oneembodiment, if there is a software bug or error in software running onprimary computer, and non-identical software code running on secondarycomputer provides same overall result, then supervisory MCU may havegreater confidence that overall result is correct, and bug in softwareor hardware on primary computer is not causing material error.

In at least one embodiment, output of ADAS system 838 may be fed intoprimary computer's perception block and/or primary computer's dynamicdriving task block. For example, in at least one embodiment, if ADASsystem 838 indicates a forward crash warning due to an objectimmediately ahead, perception block may use this information whenidentifying objects. In at least one embodiment, secondary computer mayhave its own neural network which is trained and thus reduces risk offalse positives, as described herein.

In at least one embodiment, vehicle 800 may further include infotainmentSoC 830 (e.g., an in-vehicle infotainment system (IVI)). Althoughillustrated and described as an SoC, infotainment system 830, in atleast one embodiment, may not be an SoC, and may include, withoutlimitation, two or more discrete components. In at least one embodiment,infotainment SoC 830 may include, without limitation, a combination ofhardware and software that may be used to provide audio (e.g., music, apersonal digital assistant, navigational instructions, news, radio,etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g.,hands-free calling), network connectivity (e.g., LTE, WiFi, etc.),and/or information services (e.g., navigation systems, rear-parkingassistance, a radio data system, vehicle related information such asfuel level, total distance covered, brake fuel level, oil level, dooropen/close, air filter information, etc.) to vehicle 800. For example,infotainment SoC 830 could include radios, disk players, navigationsystems, video players, USB and Bluetooth connectivity, carputers,in-car entertainment, WiFi, steering wheel audio controls, hands freevoice control, a heads-up display (“HUD”), HMI display 834, a telematicsdevice, a control panel (e.g., for controlling and/or interacting withvarious components, features, and/or systems), and/or other components.In at least one embodiment, infotainment SoC 830 may further be used toprovide information (e.g., visual and/or audible) to user(s) of vehicle,such as information from ADAS system 838, autonomous driving informationsuch as planned vehicle maneuvers, trajectories, surrounding environmentinformation (e.g., intersection information, vehicle information, roadinformation, etc.), and/or other information.

In at least one embodiment, infotainment SoC 830 may include any amountand type of GPU functionality. In at least one embodiment, infotainmentSoC 830 may communicate over bus 802 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of vehicle 800. In atleast one embodiment, infotainment SoC 830 may be coupled to asupervisory MCU such that GPU of infotainment system may perform someself-driving functions in event that primary controller(s) 836 (e.g.,primary and/or backup computers of vehicle 800) fail. In at least oneembodiment, infotainment SoC 830 may put vehicle 800 into a chauffeur tosafe stop mode, as described herein.

In at least one embodiment, vehicle 800 may further include instrumentcluster 832 (e.g., a digital dash, an electronic instrument cluster, adigital instrument panel, etc.). instrument cluster 832 may include,without limitation, a controller and/or supercomputer (e.g., a discretecontroller or supercomputer). In at least one embodiment, instrumentcluster 832 may include, without limitation, any number and combinationof a set of instrumentation such as a speedometer, fuel level, oilpressure, tachometer, odometer, turn indicators, gearshift positionindicator, seat belt warning light(s), parking-brake warning light(s),engine-malfunction light(s), supplemental restraint system (e.g.,airbag) information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among infotainment SoC 830 and instrumentcluster 832. In at least one embodiment, instrument cluster 832 may beincluded as part of infotainment SoC 830, or vice versa.

In at least one embodiment, signals received by antennas 208 of FIG. 2may be from vehicle 800 and processed as described with respect to atleast one of FIGS. 1-6 to provide information to vehicle 800 for itsautonomous operation, such as weather data, navigational data, roadcondition data, and/or may be used to provide a remote operator anability to control vehicle 800 remotely.

FIG. 8D is a diagram of a system 876 for communication betweencloud-based server(s) and autonomous vehicle 800 of FIG. 8A, accordingto at least one embodiment. In at least one embodiment, system 876 mayinclude, without limitation, server(s) 878, network(s) 890, and anynumber and type of vehicles, including vehicle 800. Server(s) 878 mayinclude, without limitation, a plurality of GPUs 884(A)-884(H)(collectively referred to herein as GPUs 884), PCIe switches882(A)-882(H) (collectively referred to herein as PCIe switches 882),and/or CPUs 880(A)-880(B) (collectively referred to herein as CPUs 880).GPUs 884, CPUs 880, and PCIe switches 882 may be interconnected withhigh-speed interconnects such as, for example and without limitation,NVLink interfaces 888 developed by NVIDIA and/or PCIe connections 886.In at least one embodiment, GPUs 884 are connected via an NVLink and/orNVSwitch SoC and GPUs 884 and PCIe switches 882 are connected via PCIeinterconnects. In at least one embodiment, although eight GPUs 884, twoCPUs 880, and four PCIe switches 882 are illustrated, this is notintended to be limiting. In at least one embodiment, each of server(s)878 may include, without limitation, any number of GPUs 884, CPUs 880,and/or PCIe switches 882, in any combination. For example, in at leastone embodiment, server(s) 878 could each include eight, sixteen,thirty-two, and/or more GPUs 884.

In at least one embodiment, server(s) 878 may receive, over network(s)890 and from vehicles, image data representative of images showingunexpected or changed road conditions, such as recently commencedroad-work. In at least one embodiment, server(s) 878 may transmit, overnetwork(s) 890 and to vehicles, neural networks 892, updated neuralnetworks 892, and/or map information 894, including, without limitation,information regarding traffic and road conditions. In at least oneembodiment, updates to map information 894 may include, withoutlimitation, updates for HD map 822, such as information regardingconstruction sites, potholes, detours, flooding, and/or otherobstructions. In at least one embodiment, neural networks 892, updatedneural networks 892, and/or map information 894 may have resulted fromnew training and/or experiences represented in data received from anynumber of vehicles in environment, and/or based at least in part ontraining performed at a data center (e.g., using server(s) 878 and/orother servers).

In at least one embodiment, server(s) 878 may be used to train machinelearning models (e.g., neural networks) based at least in part ontraining data. Training data may be generated by vehicles, and/or may begenerated in a simulation (e.g., using a game engine). In at least oneembodiment, any amount of training data is tagged (e.g., whereassociated neural network benefits from supervised learning) and/orundergoes other pre-processing. In at least one embodiment, any amountof training data is not tagged and/or pre-processed (e.g., whereassociated neural network does not require supervised learning). In atleast one embodiment, once machine learning models are trained, machinelearning models may be used by vehicles (e.g., transmitted to vehiclesover network(s) 890, and/or machine learning models may be used byserver(s) 878 to remotely monitor vehicles.

In at least one embodiment, server(s) 878 may receive data from vehiclesand apply data to up-to-date real-time neural networks for real-timeintelligent inferencing. In at least one embodiment, server(s) 878 mayinclude deep-learning supercomputers and/or dedicated AI computerspowered by GPU(s) 884, such as a DGX and DGX Station machines developedby NVIDIA. However, in at least one embodiment, server(s) 878 mayinclude deep learning infrastructure that use CPU-powered data centers.

In at least one embodiment, deep-learning infrastructure of server(s)878 may be capable of fast, real-time inferencing, and may use thatcapability to evaluate and verify health of processors, software, and/orassociated hardware in vehicle 800. For example, in at least oneembodiment, deep-learning infrastructure may receive periodic updatesfrom vehicle 800, such as a sequence of images and/or objects thatvehicle 800 has located in that sequence of images (e.g., via computervision and/or other machine learning object classification techniques).In at least one embodiment, deep-learning infrastructure may run its ownneural network to identify objects and compare them with objectsidentified by vehicle 800 and, if results do not match and deep-learninginfrastructure concludes that AI in vehicle 800 is malfunctioning, thenserver(s) 878 may transmit a signal to vehicle 800 instructing afail-safe computer of vehicle 800 to assume control, notify passengers,and complete a safe parking maneuver.

In at least one embodiment, server(s) 878 may include GPU(s) 884 and oneor more programmable inference accelerators (e.g., NVIDIA's TensorRT 3).In at least one embodiment, combination of GPU-powered servers andinference acceleration may make real-time responsiveness possible. In atleast one embodiment, such as where performance is less critical,servers powered by CPUs, FPGAs, and other processors may be used forinferencing.

Computer Systems

FIG. 9 is a block diagram illustrating an exemplary computer system,which may be a system with interconnected devices and components, asystem-on-a-chip (SOC) or some combination thereof 900 formed with aprocessor that may include execution units to execute an instruction,according to at least one embodiment. In at least one embodiment,computer system 900 may include, without limitation, a component, suchas a processor 902 to employ execution units including logic to performalgorithms for process data, in accordance with present disclosure, suchas in embodiment described herein. In at least one embodiment, computersystem 900 may include processors, such as PENTIUM® Processor family,Xeon™, Itanium®, XScale™ and/or StrongARM™, Intel® Core™, or Intel®Nervana™ microprocessors available from Intel Corporation of SantaClara, Calif., although other systems (including PCs having othermicroprocessors, engineering workstations, set-top boxes and like) mayalso be used. In at least one embodiment, computer system 900 mayexecute a version of WINDOWS' operating system available from MicrosoftCorporation of Redmond, Wash., although other operating systems (UNIXand Linux for example), embedded software, and/or graphical userinterfaces, may also be used.

Embodiments may be used in other devices such as handheld devices andembedded applications. Some examples of handheld devices includecellular phones, Internet Protocol devices, digital cameras, personaldigital assistants (“PDAs”), and handheld PCs. In at least oneembodiment, embedded applications may include a microcontroller, adigital signal processor (“DSP”), system on a chip, network computers(“NetPCs”), set-top boxes, network hubs, wide area network (“WAN”)switches, or any other system that may perform one or more instructionsin accordance with at least one embodiment.

In at least one embodiment, computer system 900 may include, withoutlimitation, processor 902 that may include, without limitation, one ormore execution units 908 to perform machine learning model trainingand/or inferencing according to techniques described herein. In at leastone embodiment, system 900 is a single processor desktop or serversystem, but in another embodiment system 900 may be a multiprocessorsystem. In at least one embodiment, processor 902 may include, withoutlimitation, a complex instruction set computer (“CISC”) microprocessor,a reduced instruction set computing (“RISC”) microprocessor, a very longinstruction word (“VLIW”) microprocessor, a processor implementing acombination of instruction sets, or any other processor device, such asa digital signal processor, for example. In at least one embodiment,processor 902 may be coupled to a processor bus 910 that may transmitdata signals between processor 902 and other components in computersystem 900.

In at least one embodiment, processor 902 may include, withoutlimitation, a Level 1 (“L1”) internal cache memory (“cache”) 904. In atleast one embodiment, processor 902 may have a single internal cache ormultiple levels of internal cache. In at least one embodiment, cachememory may reside external to processor 902. Other embodiments may alsoinclude a combination of both internal and external caches depending onparticular implementation and needs. In at least one embodiment,register file 906 may store different types of data in various registersincluding, without limitation, integer registers, floating pointregisters, status registers, and instruction pointer register.

In at least one embodiment, execution unit 908, including, withoutlimitation, logic to perform integer and floating point operations, alsoresides in processor 902. Processor 902 may also include a microcode(“ucode”) read only memory (“ROM”) that stores microcode for certainmacro instructions. In at least one embodiment, execution unit 908 mayinclude logic to handle a packed instruction set 909. In at least oneembodiment, by including packed instruction set 909 in instruction setof a general-purpose processor 902, along with associated circuitry toexecute instructions, operations used by many multimedia applicationsmay be performed using packed data in a general-purpose processor 902.In one or more embodiments, many multimedia applications may beaccelerated and executed more efficiently by using full width of aprocessor's data bus for performing operations on packed data, which mayeliminate need to transfer smaller units of data across processor's databus to perform one or more operations one data element at a time.

In at least one embodiment, execution unit 908 may also be used inmicrocontrollers, embedded processors, graphics devices, DSPs, and othertypes of logic circuits. In at least one embodiment, computer system 900may include, without limitation, a memory 920. In at least oneembodiment, memory 920 may be implemented as a Dynamic Random AccessMemory (“DRAM”) device, a Static Random Access Memory (“SRAM”) device,flash memory device, or other memory device. Memory 920 may storeinstruction(s) 919 and/or data 921 represented by data signals that maybe executed by processor 902.

In at least one embodiment, system logic chip may be coupled toprocessor bus 910 and memory 920. In at least one embodiment, systemlogic chip may include, without limitation, a memory controller hub(“MCH”) 916, and processor 902 may communicate with MCH 916 viaprocessor bus 910. In at least one embodiment, MCH 916 may provide ahigh bandwidth memory path 918 to memory 920 for instruction and datastorage and for storage of graphics commands, data and textures. In atleast one embodiment, MCH 916 may direct data signals between processor902, memory 920, and other components in computer system 900 and tobridge data signals between processor bus 910, memory 920, and a systemI/O 922. In at least one embodiment, system logic chip may provide agraphics port for coupling to a graphics controller. In at least oneembodiment, MCH 916 may be coupled to memory 920 through a highbandwidth memory path 918 and graphics/video card 912 may be coupled toMCH 916 through an Accelerated Graphics Port (“AGP”) interconnect 914.

In at least one embodiment, computer system 900 may use system I/O 922that is a proprietary hub interface bus to couple MCH 916 to I/Ocontroller hub (“ICH”) 930. In at least one embodiment, ICH 930 mayprovide direct connections to some I/O devices via a local I/O bus. Inat least one embodiment, local I/O bus may include, without limitation,a high-speed I/O bus for connecting peripherals to memory 920, chipset,and processor 902. Examples may include, without limitation, an audiocontroller 929, a firmware hub (“flash BIOS”) 928, a wirelesstransceiver 926, a data storage 924, a legacy I/O controller 923containing user input and keyboard interfaces, a serial expansion port927, such as Universal Serial Bus (“USB”), and a network controller 934.data storage 924 may comprise a hard disk drive, a floppy disk drive, aCD-ROM device, a flash memory device, or other mass storage device.

In at least one embodiment, FIG. 9 illustrates a system, which includesinterconnected hardware devices or “chips”, whereas in otherembodiments, FIG. 9 may illustrate an exemplary System on a Chip(“SoC”). In at least one embodiment, devices illustrated in FIG. 9 maybe interconnected with proprietary interconnects, standardizedinterconnects (e.g., PCIe) or some combination thereof. In at least oneembodiment, one or more components of system 900 are interconnectedusing compute express link (CXL) interconnects.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 9 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one of processor 902 and graphics card 912 are usedto cause information received from a plurality of 5G new radio antennasto be decoded in parallel by a plurality of processor pipelines. In atleast one embodiment, at least one of processor 902 and graphics card912 are used to layer demap, descramble, and de-rate-match 5G NR PUSCHdata that has been soft demapped in preparation for LDPC decoding, whereat least one thread block is used to perform operations on code blockdata elements (e.g., LLRs) in parallel. In at least one embodiment,processor 902 executes a kernel launch function that passes parametersto at least one kernel on graphics card 912 that layer demaps,descrambles, and de-rate matches data from 5G NR antennas in parallel.

FIG. 10 is a block diagram illustrating an electronic device 1000 forutilizing a processor 1010, according to at least one embodiment. In atleast one embodiment, electronic device 1000 may be, for example andwithout limitation, a notebook, a tower server, a rack server, a bladeserver, a laptop, a desktop, a tablet, a mobile device, a phone, anembedded computer, or any other suitable electronic device.

In at least one embodiment, system 1000 may include, without limitation,processor 1010 communicatively coupled to any suitable number or kind ofcomponents, peripherals, modules, or devices. In at least oneembodiment, processor 1010 coupled using a bus or interface, such as a1° C. bus, a System Management Bus (“SMBus”), a Low Pin Count (LPC) bus,a Serial Peripheral Interface (“SPI”), a High Definition Audio (“HDA”)bus, a Serial Advance Technology Attachment (“SATA”) bus, a UniversalSerial Bus (“USB”) (versions 1, 2, 3), or a Universal AsynchronousReceiver/Transmitter (“UART”) bus. In at least one embodiment, FIG. 10illustrates a system, which includes interconnected hardware devices or“chips”, whereas in other embodiments, FIG. 10 may illustrate anexemplary System on a Chip (“SoC”). In at least one embodiment, devicesillustrated in FIG. 10 may be interconnected with proprietaryinterconnects, standardized interconnects (e.g., PCIe) or somecombination thereof. In at least one embodiment, one or more componentsof FIG. 10 are interconnected using compute express link (CXL)interconnects.

In at least one embodiment, FIG. 10 may include a display 1024, a touchscreen 1025, a touch pad 1030, a Near Field Communications unit (“NFC”)1045, a sensor hub 1040, a thermal sensor 1046, an Express Chipset(“EC”) 1035, a Trusted Platform Module (“TPM”) 1038, BIOS/firmware/flashmemory (“BIOS, FW Flash”) 1022, a DSP 1060, a drive “SSD or HDD”) 1020such as a Solid State Disk (“SSD”) or a Hard Disk Drive (“HDD”), awireless local area network unit (“WLAN”) 1050, a Bluetooth unit 1052, aWireless Wide Area Network unit (“WWAN”) 1056, a Global PositioningSystem (GPS) 1055, a camera (“USB 3.0 camera”) 1054 such as a USB 3.0camera, or a Low Power Double Data Rate (“LPDDR”) memory unit (“LPDDR3”)1015 implemented in, for example, LPDDR3 standard. These components mayeach be implemented in any suitable manner.

In at least one embodiment, other components may be communicativelycoupled to processor 1010 through components discussed above. In atleast one embodiment, an accelerometer 1041, Ambient Light Sensor(“ALS”) 1042, compass 1043, and a gyroscope 1044 may be communicativelycoupled to sensor hub 1040. In at least one embodiment, thermal sensor1039, a fan 1037, a keyboard 1046, and a touch pad 1030 may becommunicatively coupled to EC 1035. In at least one embodiment, speaker1063, a headphones 1064, and a microphone (“mic”) 1065 may becommunicatively coupled to an audio unit (“audio codec and class d amp”)1064, which may in turn be communicatively coupled to DSP 1060. In atleast one embodiment, audio unit 1064 may include, for example andwithout limitation, an audio coder/decoder (“codec”) and a class Damplifier. In at least one embodiment, SIM card (“SIM”) 1057 may becommunicatively coupled to WWAN unit 1056. In at least one embodiment,components such as WLAN unit 1050 and Bluetooth unit 1052, as well asWWAN unit 1056 may be implemented in a Next Generation Form Factor(“NGFF”).

In at least one embodiment, at least one component shown or describedwith respect to FIG. 10 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, processor 1010 is used to cause information received from aplurality of 5G new radio antennas to be decoded in parallel by aplurality of processor pipelines of processor 1010. In at least oneembodiment processor 1010 is used to layer demap, descramble, andde-rate-match 5G NR PUSCH data that has been soft demapped inpreparation for LDPC decoding, where at least one thread block is usedto perform operations on code block data elements (e.g., LLRs) inparallel.

FIG. 11 illustrates a computer system 1100, according to at least oneembodiment. In at least one embodiment, computer system 1100 isconfigured to implement various processes and methods describedthroughout this disclosure.

In at least one embodiment, computer system 1100 comprises, withoutlimitation, at least one central processing unit (“CPU”) 1102 that isconnected to a communication bus 1110 implemented using any suitableprotocol, such as PCI (“Peripheral Component Interconnect”), peripheralcomponent interconnect express (“PCI-Express”), AGP (“AcceleratedGraphics Port”), HyperTransport, or any other bus or point-to-pointcommunication protocol(s). In at least one embodiment, computer system1100 includes, without limitation, a main memory 1104 and control logic(e.g., implemented as hardware, software, or a combination thereof) anddata are stored in main memory 1104 which may take form of random accessmemory (“RAM”). In at least one embodiment, a network interfacesubsystem (“network interface”) 1122 provides an interface to othercomputing devices and networks for receiving data from and transmittingdata to other systems from computer system 1100.

In at least one embodiment, computer system 1100, in at least oneembodiment, includes, without limitation, input devices 1108, parallelprocessing system 1112, and display devices 1106 which can beimplemented using a conventional cathode ray tube (“CRT”), liquidcrystal display (“LCD”), light emitting diode (“LED”), plasma display,or other suitable display technologies. In at least one embodiment, userinput is received from input devices 1108 such as keyboard, mouse,touchpad, microphone, and more. In at least one embodiment, each offoregoing modules can be situated on a single semiconductor platform toform a processing system.

In at least one embodiment, computer programs in form ofmachine-readable executable code or computer control logic algorithmsare stored in main memory 1104 and/or secondary storage. Computerprograms, if executed by one or more processors, enable system 1100 toperform various functions in accordance with at least one embodiment.memory 1104, storage, and/or any other storage are possible examples ofcomputer-readable media. In at least one embodiment, secondary storagemay refer to any suitable storage device or system such as a hard diskdrive and/or a removable storage drive, representing a floppy diskdrive, a magnetic tape drive, a compact disk drive, digital versatiledisk (“DVD”) drive, recording device, universal serial bus (“USB”) flashmemory, etc. In at least one embodiment, architecture and/orfunctionality of various previous figures are implemented in context ofCPU 1102; parallel processing system 1112; an integrated circuit capableof at least a portion of capabilities of both CPU 1102; parallelprocessing system 1112; a chipset (e.g., a group of integrated circuitsdesigned to work and sold as a unit for performing related functions,etc.); and any suitable combination of integrated circuit(s).

In at least one embodiment, architecture and/or functionality of variousprevious figures are implemented in context of a general computersystem, a circuit board system, a game console system dedicated forentertainment purposes, an application-specific system, and more. In atleast one embodiment, computer system 1100 may take form of a desktopcomputer, a laptop computer, a tablet computer, servers, supercomputers,a smart-phone (e.g., a wireless, hand-held device), personal digitalassistant (“PDA”), a digital camera, a vehicle, a head mounted display,a hand-held electronic device, a mobile phone device, a television,workstation, game consoles, embedded system, and/or any other type oflogic.

In at least one embodiment, parallel processing system 1112 includes,without limitation, a plurality of parallel processing units (“PPUs”)1114 and associated memories 1116. In at least one embodiment, PPUs 1114are connected to a host processor or other peripheral devices via aninterconnect 1118 and a switch 1120 or multiplexer. In at least oneembodiment, parallel processing system 1112 distributes computationaltasks across PPUs 1114 which can be parallelizable—for example, as partof distribution of computational tasks across multiple graphicsprocessing unit (“GPU”) thread blocks. In at least one embodiment,memory is shared and accessible (e.g., for read and/or write access)across some or all of PPUs 1114, although such shared memory may incurperformance penalties relative to use of local memory and registersresident to a PPU 1114. In at least one embodiment, operation of PPUs1114 is synchronized through use of a command such as_syncthreads( ),wherein all threads in a block (e.g., executed across multiple PPUs1114) to reach a certain point of execution of code before proceeding.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 11 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one of parallel processing system 1112 and CPU 1102are used to cause information received from a plurality of 5G new radioantennas to be decoded in parallel by a plurality of processorpipelines. In at least one embodiment, at least one PPU 1114 of parallelprocessing system 1112 is used to layer demap, descramble, andde-rate-match 5G NR PUSCH data that has been soft demapped inpreparation for LDPC decoding, where at least one thread block is usedto perform operations on code block data elements (e.g., LLRs) inparallel. In at least one embodiment, CPU 1102 executes a kernel launchfunction that passes parameters to at least one kernel on PPUs 1114 thatlayer demaps, descrambles, and de-rate matches data from 5G NR antennasin parallel.

FIG. 12 illustrates a computer system 1200, according to at least oneembodiment. In at least one embodiment, computer system 1200 includes,without limitation, a computer 1210 and a USB stick 1220. In at leastone embodiment, computer 1210 may include, without limitation, anynumber and type of processor(s) (not shown) and a memory (not shown). Inat least one embodiment, computer 1210 includes, without limitation, aserver, a cloud instance, a laptop, and a desktop computer.

In at least one embodiment, USB stick 1220 includes, without limitation,a processing unit 1230, a USB interface 1240, and USB interface logic1250. In at least one embodiment, processing unit 1230 may be anyinstruction execution system, apparatus, or device capable of executinginstructions. In at least one embodiment, processing unit 1230 mayinclude, without limitation, any number and type of processing cores(not shown). In at least one embodiment, processing core 1230 comprisesan application specific integrated circuit (“ASIC”) that is optimized toperform any amount and type of operations associated with machinelearning. For instance, in at least one embodiment, processing core 1230is a tensor processing unit (“TPC”) that is optimized to perform machinelearning inference operations. In at least one embodiment, processingcore 1230 is a vision processing unit (“VPU”) that is optimized toperform machine vision and machine learning inference operations.

In at least one embodiment, USB interface 1240 may be any type of USBconnector or USB socket. For instance, in at least one embodiment, USBinterface 1240 is a USB 3.0 Type-C socket for data and power. In atleast one embodiment, USB interface 1240 is a USB 3.0 Type-A connector.In at least one embodiment, USB interface logic 1250 may include anyamount and type of logic that enables processing unit 1230 to interfacewith or devices (e.g., computer 1210) via USB connector 1240.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 12 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, computer 1210 is used to cause information received from aplurality of 5G new radio antennas to be decoded in parallel by aplurality of processor pipelines. In at least one embodiment, computer1210 is used to layer demap, descramble, and de-rate-match 5G NR PUSCHdata that has been soft demapped in preparation for LDPC decoding, whereat least one thread block is used to perform operations on code blockdata elements (e.g., LLRs) in parallel.

FIG. 13A illustrates an exemplary architecture in which a plurality ofGPUs 1310-1313 is communicatively coupled to a plurality of multi-coreprocessors 1305-1306 over high-speed links 1340-1343 (e.g., buses,point-to-point interconnects, etc.). In one embodiment, high-speed links1340-1343 support a communication throughput of 4 GB/s, 30 GB/s, 80 GB/sor higher. Various interconnect protocols may be used including, but notlimited to, PCIe 4.0 or 5.0 and NVLink 2.0.

In addition, and in one embodiment, two or more of GPUs 1310-1313 areinterconnected over high-speed links 1329-1330, which may be implementedusing same or different protocols/links than those used for high-speedlinks 1340-1343. Similarly, two or more of multi-core processors1305-1306 may be connected over high speed link 1328 which may besymmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s, 120GB/s or higher. Alternatively, all communication between various systemcomponents shown in FIG. 13A may be accomplished using sameprotocols/links (e.g., over a common interconnection fabric).

In one embodiment, each multi-core processor 1305-1306 iscommunicatively coupled to a processor memory 1301-1302, via memoryinterconnects 1326-1327, respectively, and each GPU 1310-1313 iscommunicatively coupled to GPU memory 1320-1323 over GPU memoryinterconnects 1350-1353, respectively. Memory interconnects 1326-1327and 1350-1353 may utilize same or different memory access technologies.By way of example, and not limitation, processor memories 1301-1302 andGPU memories 1320-1323 may be volatile memories such as dynamic randomaccess memories (DRAMs) (including stacked DRAMs), Graphics DDR SDRAM(GDDR) (e.g., GDDR5, GDDR6), or High Bandwidth Memory (HBM) and/or maybe non-volatile memories such as 3D XPoint or Nano-Ram. In oneembodiment, some portion of processor memories 1301-1302 may be volatilememory and another portion may be non-volatile memory (e.g., using atwo-level memory (2LM) hierarchy).

As described herein, although various processors 1305-1306 and GPUs1310-1313 may be physically coupled to a particular memory 1301-1302,1320-1323, respectively, a unified memory architecture may beimplemented in which a same virtual system address space (also referredto as “effective address” space) is distributed among various physicalmemories. For example, processor memories 1301-1302 may each comprise 64GB of system memory address space and GPU memories 1320-1323 may eachcomprise 32 GB of system memory address space (resulting in a total of256 GB addressable memory in this example).

FIG. 13B illustrates additional details for an interconnection between amulti-core processor 1307 and a graphics acceleration module 1346 inaccordance with one exemplary embodiment. Graphics acceleration module1346 may include one or more GPU chips integrated on a line card whichis coupled to processor 1307 via high-speed link 1340. Alternatively,graphics acceleration module 1346 may be integrated on a same package orchip as processor 1307.

In at least one embodiment, illustrated processor 1307 includes aplurality of cores 1360A-1360D, each with a translation lookaside buffer1361A-1361D and one or more caches 1362A-1362D. In at least oneembodiment, cores 1360A-1360D may include various other components forexecuting instructions and processing data which are not illustrated.Caches 1362A-1362D may comprise level 1 (L1) and level 2 (L2) caches. Inaddition, one or more shared caches 1356 may be included in caches1362A-1362D and shared by sets of cores 1360A-1360D. For example, oneembodiment of processor 1307 includes 24 cores, each with its own L1cache, twelve shared L2 caches, and twelve shared L3 caches. In thisembodiment, one or more L2 and L3 caches are shared by two adjacentcores. Processor 1307 and graphics acceleration module 1346 connect withsystem memory 1314, which may include processor memories 1301-1302 ofFIG. 13A.

Coherency is maintained for data and instructions stored in variouscaches 1362A-1362D, 1356 and system memory 1314 via inter-corecommunication over a coherence bus 1364. For example, each cache mayhave cache coherency logic/circuitry associated therewith to communicateto over coherence bus 1364 in response to detected reads or writes toparticular cache lines. In one implementation, a cache snooping protocolis implemented over coherence bus 1364 to snoop cache accesses.

In one embodiment, a proxy circuit 1325 communicatively couples graphicsacceleration module 1346 to coherence bus 1364, allowing graphicsacceleration module 1346 to participate in a cache coherence protocol asa peer of cores 1360A-1360D. In particular, an interface 1335 providesconnectivity to proxy circuit 1325 over high-speed link 1340 (e.g., aPCIe bus, NVLink, etc.) and an interface 1337 connects graphicsacceleration module 1346 to link 1340.

In one implementation, an accelerator integration circuit 1336 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 1331, 1332, N of graphics acceleration module 1346. Graphicsprocessing engines 1331, 1332, N may each comprise a separate graphicsprocessing unit (GPU). Alternatively, graphics processing engines 1331,1332, N may comprise different types of graphics processing engineswithin a GPU such as graphics execution units, media processing engines(e.g., video encoders/decoders), samplers, and blit engines. In at leastone embodiment, graphics acceleration module 1346 may be a GPU with aplurality of graphics processing engines 1331-1332, N or graphicsprocessing engines 1331-1332, N may be individual GPUs integrated on acommon package, line card, or chip.

In one embodiment, accelerator integration circuit 1336 includes amemory management unit (MMU) 1339 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 1314. MMU 1339 may alsoinclude a translation lookaside buffer (TLB) (not shown) for cachingvirtual/effective to physical/real address translations. In oneimplementation, a cache 1338 stores commands and data for efficientaccess by graphics processing engines 1331-1332, N. In one embodiment,data stored in cache 1338 and graphics memories 1333-1334, M is keptcoherent with core caches 1362A-1362D, 1356 and system memory 1314. Asmentioned, this may be accomplished via proxy circuit 1325 on behalf ofcache 1338 and memories 1333-1334, M (e.g., sending updates to cache1338 related to modifications/accesses of cache lines on processorcaches 1362A-1362D, 1356 and receiving updates from cache 1338).

A set of registers 1345 store context data for threads executed bygraphics processing engines 1331-1332, N and a context managementcircuit 1348 manages thread contexts. For example, context managementcircuit 1348 may perform save and restore operations to save and restorecontexts of various threads during contexts switches (e.g., where afirst thread is saved and a second thread is stored so that a secondthread can be execute by a graphics processing engine). For example, ona context switch, context management circuit 1348 may store currentregister values to a designated region in memory (e.g., identified by acontext pointer). It may then restore register values when returning toa context. In one embodiment, an interrupt management circuit 1347receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 1331 are translated to real/physical addresses insystem memory 1314 by MMU 1339. One embodiment of acceleratorintegration circuit 1336 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 1346 and/or other accelerator devices. Graphicsaccelerator module 1346 may be dedicated to a single applicationexecuted on processor 1307 or may be shared between multipleapplications. In one embodiment, a virtualized graphics executionenvironment is presented in which resources of graphics processingengines 1331-1332, N are shared with multiple applications or virtualmachines (VMs). In at least one embodiment, resources may be subdividedinto “slices” which are allocated to different VMs and/or applicationsbased on processing requirements and priorities associated with VMsand/or applications.

In at least one embodiment, accelerator integration circuit 1336performs as a bridge to a system for graphics acceleration module 1346and provides address translation and system memory cache services. Inaddition, accelerator integration circuit 1336 may providevirtualization facilities for a host processor to manage virtualizationof graphics processing engines 1331-1332, interrupts, and memorymanagement.

Because hardware resources of graphics processing engines 1331-1332, Nare mapped explicitly to a real address space seen by host processor1307, any host processor can address these resources directly using aneffective address value. One function of accelerator integration circuit1336, in one embodiment, is physical separation of graphics processingengines 1331-1332, N so that they appear to a system as independentunits.

In at least one embodiment, one or more graphics memories 1333-1334, Mare coupled to each of graphics processing engines 1331-1332, N,respectively. Graphics memories 1333-1334, M store instructions and databeing processed by each of graphics processing engines 1331-1332, N.Graphics memories 1333-1334, M may be volatile memories such as DRAMs(including stacked DRAMs), GDDR memory (e.g., GDDR5, GDDR6), or HBM,and/or may be non-volatile memories such as 3D XPoint or Nano-Ram.

In one embodiment, to reduce data traffic over link 1340, biasingtechniques are used to ensure that data stored in graphics memories1333-1334, M is data which will be used most frequently by graphicsprocessing engines 1331-1332, N and preferably not used by cores1360A-1360D (at least not frequently). Similarly, a biasing mechanismattempts to keep data needed by cores (and preferably not graphicsprocessing engines 1331-1332, N) within caches 1362A-1362D, 1356 ofcores and system memory 1314.

FIG. 13C illustrates another exemplary embodiment in which acceleratorintegration circuit 1336 is integrated within processor 1307. In thisembodiment, graphics processing engines 1331-1332, N communicatedirectly over high-speed link 1340 to accelerator integration circuit1336 via interface 1337 and interface 1335 (which, again, may be utilizeany form of bus or interface protocol). Accelerator integration circuit1336 may perform same operations as those described with respect to FIG.13B, but potentially at a higher throughput given its close proximity tocoherence bus 1364 and caches 1362A-1362D, 1356. One embodiment supportsdifferent programming models including a dedicated-process programmingmodel (no graphics acceleration module virtualization) and sharedprogramming models (with virtualization), which may include programmingmodels which are controlled by accelerator integration circuit 1336 andprogramming models which are controlled by graphics acceleration module1346.

In at least one embodiment, graphics processing engines 1331-1332, N arededicated to a single application or process under a single operatingsystem. In at least one embodiment, a single application can funnelother application requests to graphics processing engines 1331-1332, N,providing virtualization within a VM/partition.

In at least one embodiment, graphics processing engines 1331-1332, N,may be shared by multiple VM/application partitions. In at least oneembodiment, shared models may use a system hypervisor to virtualizegraphics processing engines 1331-1332, N to allow access by eachoperating system. For single-partition systems without a hypervisor,graphics processing engines 1331-1332, N are owned by an operatingsystem. In at least one embodiment, an operating system can virtualizegraphics processing engines 1331-1332, N to provide access to eachprocess or application.

In at least one embodiment, graphics acceleration module 1346 or anindividual graphics processing engine 1331-1332, N selects a processelement using a process handle. In one embodiment, process elements arestored in system memory 1314 and are addressable using an effectiveaddress to real address translation techniques described herein. In atleast one embodiment, a process handle may be an implementation-specificvalue provided to a host process when registering its context withgraphics processing engine 1331-1332, N (that is, calling systemsoftware to add a process element to a process element linked list). Inat least one embodiment, a lower 16-bits of a process handle may be anoffset of a process element within a process element linked list.

FIG. 13D illustrates an exemplary accelerator integration slice 1390. Asused herein, a “slice” comprises a specified portion of processingresources of accelerator integration circuit 1336. Application effectiveaddress space 1382 within system memory 1314 stores process elements1383. In one embodiment, process elements 1383 are stored in response toGPU invocations 1381 from applications 1380 executed on processor 1307.A process element 1383 contains process state for correspondingapplication 1380. A work descriptor (WD) 1384 contained in processelement 1383 can be a single job requested by an application or maycontain a pointer to a queue of jobs. In at least one embodiment, WD1384 is a pointer to a job request queue in an application's addressspace 1382.

Graphics acceleration module 1346 and/or individual graphics processingengines 1331-1332, N can be shared by all or a subset of processes in asystem. In at least one embodiment, an infrastructure for setting upprocess state and sending a WD 1384 to a graphics acceleration module1346 to start a job in a virtualized environment may be included.

In at least one embodiment, a dedicated-process programming model isimplementation-specific. In this model, a single process owns graphicsacceleration module 1346 or an individual graphics processing engine1331. Because graphics acceleration module 1346 is owned by a singleprocess, a hypervisor initializes accelerator integration circuit 1336for an owning partition and an operating system initializes acceleratorintegration circuit 1336 for an owning process when graphicsacceleration module 1346 is assigned.

In operation, a WD fetch unit 1391 in accelerator integration slice 1390fetches next WD 1384 which includes an indication of work to be done byone or more graphics processing engines of graphics acceleration module1346. Data from WD 1384 may be stored in registers 1345 and used by MMU1339, interrupt management circuit 1347 and/or context managementcircuit 1348 as illustrated. For example, one embodiment of MMU 1339includes segment/page walk circuitry for accessing segment/page tables1386 within OS virtual address space 1385. Interrupt management circuit1347 may process interrupt events 1392 received from graphicsacceleration module 1346. When performing graphics operations, aneffective address 1393 generated by a graphics processing engine1331-1332, N is translated to a real address by MMU 1339.

In one embodiment, a same set of registers 1345 are duplicated for eachgraphics processing engine 1331-1332, N and/or graphics accelerationmodule 1346 and may be initialized by a hypervisor or operating system.Each of these duplicated registers may be included in an acceleratorintegration slice 1390. Exemplary registers that may be initialized by ahypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by an operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 1384 is specific to a particular graphicsacceleration module 1346 and/or graphics processing engines 1331-1332,N. It contains all information required by a graphics processing engine1331-1332, N to do work or it can be a pointer to a memory locationwhere an application has set up a command queue of work to be completed.

FIG. 13E illustrates additional details for one exemplary embodiment ofa shared model. This embodiment includes a hypervisor real address space1398 in which a process element list 1399 is stored. Hypervisor realaddress space 1398 is accessible via a hypervisor 1396 which virtualizesgraphics acceleration module engines for operating system 1395.

In at least one embodiment, shared programming models allow for all or asubset of processes from all or a subset of partitions in a system touse a graphics acceleration module 1346. There are two programmingmodels where graphics acceleration module 1346 is shared by multipleprocesses and partitions: time-sliced shared and graphics directedshared.

In this model, system hypervisor 1396 owns graphics acceleration module1346 and makes its function available to all operating systems 1395. Fora graphics acceleration module 1346 to support virtualization by systemhypervisor 1396, graphics acceleration module 1346 may adhere tofollowing: 1) An application's job request must be autonomous (that is,state does not need to be maintained between jobs), or graphicsacceleration module 1346 must provide a context save and restoremechanism. 2) An application's job request is guaranteed by graphicsacceleration module 1346 to complete in a specified amount of time,including any translation faults, or graphics acceleration module 1346provides an ability to preempt processing of a job. 3) Graphicsacceleration module 1346 must be guaranteed fairness between processeswhen operating in a directed shared programming model.

In at least one embodiment, application 1380 is required to make anoperating system 1395 system call with a graphics acceleration module1346 type, a work descriptor (WD), an authority mask register (AMR)value, and a context save/restore area pointer (CSRP). In at least oneembodiment, graphics acceleration module 1346 type describes a targetedacceleration function for a system call. In at least one embodiment,graphics acceleration module 1346 type may be a system-specific value.In at least one embodiment, WD is formatted specifically for graphicsacceleration module 1346 and can be in a form of a graphics accelerationmodule 1346 command, an effective address pointer to a user-definedstructure, an effective address pointer to a queue of commands, or anyother data structure to describe work to be done by graphicsacceleration module 1346. In one embodiment, an AMR value is an AMRstate to use for a current process. In at least one embodiment, a valuepassed to an operating system is similar to an application setting anAMR. If accelerator integration circuit 1336 and graphics accelerationmodule 1346 implementations do not support a User Authority MaskOverride Register (UAMOR), an operating system may apply a current UAMORvalue to an AMR value before passing an AMR in a hypervisor call.Hypervisor 1396 may optionally apply a current Authority Mask OverrideRegister (AMOR) value before placing an AMR into process element 1383.In at least one embodiment, CSRP is one of registers 1345 containing aneffective address of an area in an application's address space 1382 forgraphics acceleration module 1346 to save and restore context state.This pointer is optional if no state is required to be saved betweenjobs or when a job is preempted. In at least one embodiment, contextsave/restore area may be pinned system memory.

Upon receiving a system call, operating system 1395 may verify thatapplication 1380 has registered and been given authority to use graphicsacceleration module 1346. Operating system 1395 then calls hypervisor1396 with information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked) 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 Virtual address of storage segmenttable pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving a hypervisor call, hypervisor 1396 verifies thatoperating system 1395 has registered and been given authority to usegraphics acceleration module 1346. Hypervisor 1396 then puts processelement 1383 into a process element linked list for a correspondinggraphics acceleration module 1346 type. A process element may includeinformation shown in Table 4.

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 Virtual address of storage segmenttable pointer (SSTP) 7 A logical interrupt service number (LISN) 8Interrupt vector table, derived from hypervisor call parameters 9 Astate register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12Storage Descriptor Register (SDR)

In at least one embodiment, hypervisor initializes a plurality ofaccelerator integration slice 1390 registers 1345.

As illustrated in FIG. 13F, in at least one embodiment, a unified memoryis used, addressable via a common virtual memory address space used toaccess physical processor memories 1301-1302 and GPU memories 1320-1323.In this implementation, operations executed on GPUs 1310-1313 utilize asame virtual/effective memory address space to access processor memories1301-1302 and vice versa, thereby simplifying programmability. In oneembodiment, a first portion of a virtual/effective address space isallocated to processor memory 1301, a second portion to second processormemory 1302, a third portion to GPU memory 1320, and so on. In at leastone embodiment, an entire virtual/effective memory space (sometimesreferred to as an effective address space) is thereby distributed acrosseach of processor memories 1301-1302 and GPU memories 1320-1323,allowing any processor or GPU to access any physical memory with avirtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 1394A-1394Ewithin one or more of MMUs 1339A-1339E ensures cache coherence betweencaches of one or more host processors (e.g., 1305) and GPUs 1310-1313and implements biasing techniques indicating physical memories in whichcertain types of data should be stored. While multiple instances ofbias/coherence management circuitry 1394A-1394E are illustrated in FIG.13F, bias/coherence circuitry may be implemented within an MMU of one ormore host processors 1305 and/or within accelerator integration circuit1336.

One embodiment allows GPU-attached memory 1320-1323 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering performance drawbacks associated withfull system cache coherence. In at least one embodiment, an ability forGPU-attached memory 1320-1323 to be accessed as system memory withoutonerous cache coherence overhead provides a beneficial operatingenvironment for GPU offload. This arrangement allows host processor 1305software to setup operands and access computation results, withoutoverhead of tradition I/O DMA data copies. Such traditional copiesinvolve driver calls, interrupts and memory mapped I/O (MMIO) accessesthat are all inefficient relative to simple memory accesses. In at leastone embodiment, an ability to access GPU attached memory 1320-1323without cache coherence overheads can be critical to execution time ofan offloaded computation. In cases with substantial streaming writememory traffic, for example, cache coherence overhead can significantlyreduce an effective write bandwidth seen by a GPU 1310-1313. In at leastone embodiment, efficiency of operand setup, efficiency of resultsaccess, and efficiency of GPU computation may play a role in determiningeffectiveness of a GPU offload.

In at least one embodiment, selection of GPU bias and host processorbias is driven by a bias tracker data structure. A bias table may beused, for example, which may be a page-granular structure (i.e.,controlled at a granularity of a memory page) that includes 1 or 2 bitsper GPU-attached memory page. In at least one embodiment, a bias tablemay be implemented in a stolen memory range of one or more GPU-attachedmemories 1320-1323, with or without a bias cache in GPU 1310-1313 (e.g.,to cache frequently/recently used entries of a bias table).Alternatively, an entire bias table may be maintained within a GPU.

In at least one embodiment, a bias table entry associated with eachaccess to GPU-attached memory 1320-1323 is accessed prior to actualaccess to a GPU memory, causing following operations. First, localrequests from GPU 1310-1313 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 1320-1323. Localrequests from a GPU that find their page in host bias are forwarded toprocessor 1305 (e.g., over a high-speed link as discussed above). In oneembodiment, requests from processor 1305 that find a requested page inhost processor bias complete a request like a normal memory read.Alternatively, requests directed to a GPU-biased page may be forwardedto GPU 1310-1313. In at least one embodiment, a GPU may then transitiona page to a host processor bias if it is not currently using a page. Inat least one embodiment, bias state of a page can be changed either by asoftware-based mechanism, a hardware-assisted software-based mechanism,or, for a limited set of cases, a purely hardware-based mechanism.

One mechanism for changing bias state employs an API call (e.g. OpenCL),which, in turn, calls a GPU's device driver which, in turn, sends amessage (or enqueues a command descriptor) to a GPU directing it tochange a bias state and, for some transitions, perform a cache flushingoperation in a host. In at least one embodiment, cache flushingoperation is used for a transition from host processor 1305 bias to GPUbias, but is not for an opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by host processor 1305. To accessthese pages, processor 1305 may request access from GPU 1310 which mayor may not grant access right away. Thus, to reduce communicationbetween processor 1305 and GPU 1310 it is beneficial to ensure thatGPU-biased pages are those which are required by a GPU but not hostprocessor 1305 and vice versa.

In at least one embodiment, at least one component shown or describedwith respect to FIGS. 13A-F is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one GPU and/or multi-core processor shown ordescribed with respect to FIGS. 13A-F is used to cause informationreceived from a plurality of 5G new radio antennas to be decoded inparallel by a plurality of processor pipelines. In at least oneembodiment, at least one GPU, such as 1310, 1311, 1312, and/or 1313, isused to layer demap, descramble, and de-rate-match 5G NR PUSCH data thathas been soft demapped in preparation for LDPC decoding, where at leastone thread block is used to perform operations on code block dataelements (e.g., LLRs) in parallel. In at least one embodiment, amulti-core processor, such as multi-core processor 1305 executes akernel launch function that passes parameters to at least one kernel ongraphics processor, such as GPU 1310, that layer demaps, descrambles,and de-rate matches data from 5G NR antennas in parallel.

FIG. 14 illustrates exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included in at least oneembodiment, including additional graphics processors/cores, peripheralinterface controllers, or general-purpose processor cores.

FIG. 14 is a block diagram illustrating an exemplary system on a chipintegrated circuit 1400 that may be fabricated using one or more IPcores, according to at least one embodiment. In at least one embodiment,integrated circuit 1400 includes one or more application processor(s)1405 (e.g., CPUs), at least one graphics processor 1410, and mayadditionally include an image processor 1415 and/or a video processor1420, any of which may be a modular IP core. In at least one embodiment,integrated circuit 1400 includes peripheral or bus logic including a USBcontroller 1425, UART controller 1430, an SPI/SDIO controller 1435, andan I.sup.2S/I.sup.2C controller 1440. In at least one embodiment,integrated circuit 1400 can include a display device 1445 coupled to oneor more of a high-definition multimedia interface (HDMI) controller 1450and a mobile industry processor interface (MIPI) display interface 1455.In at least one embodiment, storage may be provided by a flash memorysubsystem 1460 including flash memory and a flash memory controller. Inat least one embodiment, memory interface may be provided via a memorycontroller 1465 for access to SDRAM or SRAM memory devices. In at leastone embodiment, some integrated circuits additionally include anembedded security engine 1470.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 14 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, graphics processor 1410 is used to cause informationreceived from a plurality of 5G new radio antennas to be decoded inparallel by a plurality of processor pipelines. In at least oneembodiment, graphics processor 1410 is used to layer demap, descramble,and de-rate-match 5G NR PUSCH data that has been soft demapped inpreparation for LDPC decoding, where at least one thread block is usedto perform operations on code block data elements (e.g., LLRs) inparallel. In at least one embodiment, application processor 1405executes a kernel launch function that passes parameters to at least onekernel on graphics processor 1410 that layer demaps, descrambles, andde-rate matches data from 5G NR antennas in parallel.

FIGS. 15A and 15B illustrate exemplary integrated circuits andassociated graphics processors that may be fabricated using one or moreIP cores, according to various embodiments described herein. In additionto what is illustrated, other logic and circuits may be included in atleast one embodiment, including additional graphics processors/cores,peripheral interface controllers, or general-purpose processor cores.

FIGS. 15A and 15B are block diagrams illustrating exemplary graphicsprocessors for use within an SoC, according to embodiments describedherein. FIG. 15A illustrates an exemplary graphics processor 1510 of asystem on a chip integrated circuit that may be fabricated using one ormore IP cores, according to at least one embodiment. FIG. 15Billustrates an additional exemplary graphics processor 1540 of a systemon a chip integrated circuit that may be fabricated using one or more IPcores, according to at least one embodiment. In at least one embodiment,graphics processor 1510 of FIG. 15A is a low power graphics processorcore. In at least one embodiment, graphics processor 1540 of FIG. 15B isa higher performance graphics processor core. In at least oneembodiment, each of graphics processors 1510, 1540 can be variants ofgraphics processor 1410 of FIG. 14.

In at least one embodiment, graphics processor 1510 includes a vertexprocessor 1505 and one or more fragment processor(s) 1515A-1515N (e.g.,1515A, 1515B, 1515C, 1515D, through 1515N-1, and 1515N). In at least oneembodiment, graphics processor 1510 can execute different shaderprograms via separate logic, such that vertex processor 1505 isoptimized to execute operations for vertex shader programs, while one ormore fragment processor(s) 1515A-1515N execute fragment (e.g., pixel)shading operations for fragment or pixel shader programs. In at leastone embodiment, vertex processor 1505 performs a vertex processing stageof a 3D graphics pipeline and generates primitives and vertex data. Inat least one embodiment, fragment processor(s) 1515A-1515N use primitiveand vertex data generated by vertex processor 1505 to produce aframebuffer that is displayed on a display device. In at least oneembodiment, fragment processor(s) 1515A-1515N are optimized to executefragment shader programs as provided for in an OpenGL API, which may beused to perform similar operations as a pixel shader program as providedfor in a Direct 3D API.

In at least one embodiment, graphics processor 1510 additionallyincludes one or more memory management units (MMUs) 1520A-1520B,cache(s) 1525A-1525B, and circuit interconnect(s) 1530A-1530B. In atleast one embodiment, one or more MMU(s) 1520A-1520B provide for virtualto physical address mapping for graphics processor 1510, including forvertex processor 1505 and/or fragment processor(s) 1515A-1515N, whichmay reference vertex or image/texture data stored in memory, in additionto vertex or image/texture data stored in one or more cache(s)1525A-1525B. In at least one embodiment, one or more MMU(s) 1520A-1520Bmay be synchronized with other MMUs within system, including one or moreMMUs associated with one or more application processor(s) 1405, imageprocessors 1415, and/or video processors 1420 of FIG. 14, such that eachprocessor 1405-1420 can participate in a shared or unified virtualmemory system. In at least one embodiment, one or more circuitinterconnect(s) 1530A-1530B enable graphics processor 1510 to interfacewith other IP cores within SoC, either via an internal bus of SoC or viaa direct connection.

In at least one embodiment, graphics processor 1540 includes one or moreMMU(s) 1520A-1520B, caches 1525A-1525B, and circuit interconnects1530A-1530B of graphics processor 1510 of FIG. 15A. In at least oneembodiment, graphics processor 1540 includes one or more shader core(s)1555A-1555N (e.g., 1555A, 1555B, 1555C, 1555D, 1555E, 1555F, through1555N-1, and 1555N), which provides for a unified shader corearchitecture in which a single core or type or core can execute alltypes of programmable shader code, including shader program code toimplement vertex shaders, fragment shaders, and/or compute shaders. Inat least one embodiment, a number of shader cores can vary. In at leastone embodiment, graphics processor 1540 includes an inter-core taskmanager 1545, which acts as a thread dispatcher to dispatch executionthreads to one or more shader cores 1555A-1555N and a tiling unit 1558to accelerate tiling operations for tile-based rendering, in whichrendering operations for a scene are subdivided in image space, forexample to exploit local spatial coherence within a scene or to optimizeuse of internal caches.

In at least one embodiment, at least one component shown or describedwith respect to FIGS. 15A and 15B is utilized to implement techniquesand/or functions described in connection with FIGS. 1-6. In at least oneembodiment, at least one graphics processor 1510 is used to causeinformation received from a plurality of 5G new radio antennas to bedecoded in parallel by a plurality of processor pipelines. In at leastone embodiment, at least one graphics processor 1510 is used to layerdemap, descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel.

FIGS. 16A and 16B illustrate additional exemplary graphics processorlogic according to embodiments described herein. FIG. 16A illustrates agraphics core 1600 that may be included within graphics processor 1410of FIG. 14, in at least one embodiment, and may be a unified shader core1555A-1555N as in FIG. 15B in at least one embodiment. FIG. 16Billustrates a highly-parallel general-purpose graphics processing unit1630 suitable for deployment on a multi-chip module in at least oneembodiment.

In at least one embodiment, graphics core 1600 includes a sharedinstruction cache 1602, a texture unit 1618, and a cache/shared memory1620 that are common to execution resources within graphics core 1600.In at least one embodiment, graphics core 1600 can include multipleslices 1601A-1601N or partition for each core, and a graphics processorcan include multiple instances of graphics core 1600. Slices 1601A-1601Ncan include support logic including a local instruction cache1604A-1604N, a thread scheduler 1606A-1606N, a thread dispatcher1608A-1608N, and a set of registers 1610A-1610N. In at least oneembodiment, slices 1601A-1601N can include a set of additional functionunits (AFUs 1612A-1612N), floating-point units (FPU 1614A-1614N),integer arithmetic logic units (ALUs 1616-1616N), address computationalunits (ACU 1613A-1613N), double-precision floating-point units (DPFPU1615A-1615N), and matrix processing units (MPU 1617A-1617N).

In at least one embodiment, FPUs 1614A-1614N can performsingle-precision (32 bit) and half-precision (16-bit) floating pointoperations, while DPFPUs 1615A-1615N perform double precision (64-bit)floating point operations. In at least one embodiment, ALUs 1616A-1616Ncan perform variable precision integer operations at 8-bit, 16-bit, and32-bit precision, and can be configured for mixed precision operations.In at least one embodiment, MPUs 1617A-1617N can also be configured formixed precision matrix operations, including half-precision floatingpoint and 8-bit integer operations. In at least one embodiment, MPUs1617-1617N can perform a variety of matrix operations to acceleratemachine learning application frameworks, including enabling support foraccelerated general matrix to matrix multiplication (GEMM). In at leastone embodiment, AFUs 1612A-1612N can perform additional logic operationsnot supported by floating-point or integer units, includingtrigonometric operations (e.g., Sine, Cosine, etc.).

In at least one embodiment, at least one component shown or describedwith respect to FIG. 16A is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one graphics processor 1600 is used to causeinformation received from a plurality of 5G new radio antennas to bedecoded in parallel by a plurality of processor pipelines. In at leastone embodiment, at least one graphics processor 1600 is used to layerdemap, descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel.

FIG. 16B illustrates a general-purpose processing unit (GPGPU) 1630 thatcan be configured to enable highly-parallel compute operations to beperformed by an array of graphics processing units, in at least oneembodiment. In at least one embodiment, GPGPU 1630 can be linkeddirectly to other instances of GPGPU 1630 to create a multi-GPU clusterto improve training speed for deep neural networks. In at least oneembodiment, GPGPU 1630 includes a host interface 1632 to enable aconnection with a host processor. In at least one embodiment, hostinterface 1632 is a PCI Express interface. In at least one embodiment,host interface 1632 can be a vendor specific communications interface orcommunications fabric. In at least one embodiment, GPGPU 1630 receivescommands from a host processor and uses a global scheduler 1634 todistribute execution threads associated with those commands to a set ofcompute clusters 1636A-1636H. In at least one embodiment, computeclusters 1636A-1636H share a cache memory 1638. In at least oneembodiment, cache memory 1638 can serve as a higher-level cache forcache memories within compute clusters 1636A-1636H.

In at least one embodiment, GPGPU 1630 includes memory 1644A-1644Bcoupled with compute clusters 1636A-1636H via a set of memorycontrollers 1642A-1642B. In at least one embodiment, memory 1644A-1644Bcan include various types of memory devices including dynamic randomaccess memory (DRAM) or graphics random access memory, such assynchronous graphics random access memory (SGRAM), including graphicsdouble data rate (GDDR) memory.

In at least one embodiment, compute clusters 1636A-1636H each include aset of graphics cores, such as graphics core 1600 of FIG. 16A, which caninclude multiple types of integer and floating point logic units thatcan perform computational operations at a range of precisions includingsuited for machine learning computations. For example, in at least oneembodiment, at least a subset of floating point units in each of computeclusters 1636A-1636H can be configured to perform 16-bit or 32-bitfloating point operations, while a different subset of floating pointunits can be configured to perform 64-bit floating point operations.

In at least one embodiment, multiple instances of GPGPU 1630 can beconfigured to operate as a compute cluster. In at least one embodiment,communication used by compute clusters 1636A-1636H for synchronizationand data exchange varies across embodiments. In at least one embodiment,multiple instances of GPGPU 1630 communicate over host interface 1632.In at least one embodiment, GPGPU 1630 includes an I/O hub 1639 thatcouples GPGPU 1630 with a GPU link 1640 that enables a direct connectionto other instances of GPGPU 1630. In at least one embodiment, GPU link1640 is coupled to a dedicated GPU-to-GPU bridge that enablescommunication and synchronization between multiple instances of GPGPU1630. In at least one embodiment GPU link 1640 couples with a high speedinterconnect to transmit and receive data to other GPGPUs or parallelprocessors. In at least one embodiment, multiple instances of GPGPU 1630are located in separate data processing systems and communicate via anetwork device that is accessible via host interface 1632. In at leastone embodiment GPU link 1640 can be configured to enable a connection toa host processor in addition to or as an alternative to host interface1632.

In at least one embodiment, GPGPU 1630 can be configured to train neuralnetworks. In at least one embodiment, GPGPU 1630 can be used within ainferencing platform. In at least one embodiment, in which GPGPU 1630 isused for inferencing, GPGPU may include fewer compute clusters1636A-1636H relative to when GPGPU is used for training a neuralnetwork. In at least one embodiment, memory technology associated withmemory 1644A-1644B may differ between inferencing and trainingconfigurations, with higher bandwidth memory technologies devoted totraining configurations. In at least one embodiment, inferencingconfiguration of GPGPU 1630 can support inferencing specificinstructions. For example, in at least one embodiment, an inferencingconfiguration can provide support for one or more 8-bit integer dotproduct instructions, which may be used during inferencing operationsfor deployed neural networks.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 16B is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one GPGPU 1630 is used to cause informationreceived from a plurality of 5G new radio antennas to be decoded inparallel by a plurality of processor pipelines. In at least oneembodiment, at least one GPGPU 1630 is used to layer demap, descramble,and de-rate-match 5G NR PUSCH data that has been soft demapped inpreparation for LDPC decoding, where at least one thread block is usedto perform operations on code block data elements (e.g., LLRs) inparallel.

FIG. 17 is a block diagram illustrating a computing system 1700according to at least one embodiment. In at least one embodiment,computing system 1700 includes a processing subsystem 1701 having one ormore processor(s) 1702 and a system memory 1704 communicating via aninterconnection path that may include a memory hub 1705. In at least oneembodiment, memory hub 1705 may be a separate component within a chipsetcomponent or may be integrated within one or more processor(s) 1702. Inat least one embodiment, memory hub 1705 couples with an I/O subsystem1711 via a communication link 1706. In at least one embodiment, I/Osubsystem 1711 includes an I/O hub 1707 that can enable computing system1700 to receive input from one or more input device(s) 1708. In at leastone embodiment, I/O hub 1707 can enable a display controller, which maybe included in one or more processor(s) 1702, to provide outputs to oneor more display device(s) 1710A. In at least one embodiment, one or moredisplay device(s) 1710A coupled with I/O hub 1707 can include a local,internal, or embedded display device.

In at least one embodiment, processing subsystem 1701 includes one ormore parallel processor(s) 1712 coupled to memory hub 1705 via a bus orother communication link 1713. In at least one embodiment, communicationlink 1713 may be one of any number of standards based communication linktechnologies or protocols, such as, but not limited to PCI Express, ormay be a vendor specific communications interface or communicationsfabric. In at least one embodiment, one or more parallel processor(s)1712 form a computationally focused parallel or vector processing systemthat can include a large number of processing cores and/or processingclusters, such as a many integrated core (MIC) processor. In at leastone embodiment, one or more parallel processor(s) 1712 form a graphicsprocessing subsystem that can output pixels to one of one or moredisplay device(s) 1710A coupled via I/O Hub 1707. In at least oneembodiment, one or more parallel processor(s) 1712 can also include adisplay controller and display interface (not shown) to enable a directconnection to one or more display device(s) 1710B.

In at least one embodiment, a system storage unit 1714 can connect toI/O hub 1707 to provide a storage mechanism for computing system 1700.In at least one embodiment, an I/O switch 1716 can be used to provide aninterface mechanism to enable connections between I/O hub 1707 and othercomponents, such as a network adapter 1718 and/or wireless networkadapter 1719 that may be integrated into platform, and various otherdevices that can be added via one or more add-in device(s) 1720. In atleast one embodiment, network adapter 1718 can be an Ethernet adapter oranother wired network adapter. In at least one embodiment, wirelessnetwork adapter 1719 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

In at least one embodiment, computing system 1700 can include othercomponents not explicitly shown, including USB or other portconnections, optical storage drives, video capture devices, and like,may also be connected to I/O hub 1707. In at least one embodiment,communication paths interconnecting various components in FIG. 17 may beimplemented using any suitable protocols, such as PCI (PeripheralComponent Interconnect) based protocols (e.g., PCI-Express), or otherbus or point-to-point communication interfaces and/or protocol(s), suchas NV-Link high-speed interconnect, or interconnect protocols.

In at least one embodiment, one or more parallel processor(s) 1712incorporate circuitry optimized for graphics and video processing,including, for example, video output circuitry, and constitutes agraphics processing unit (GPU). In at least one embodiment, one or moreparallel processor(s) 1712 incorporate circuitry optimized for generalpurpose processing. In at least embodiment, components of computingsystem 1700 may be integrated with one or more other system elements ona single integrated circuit. For example, in at least one embodiment,one or more parallel processor(s) 1712, memory hub 1705, processor(s)1702, and I/O hub 1707 can be integrated into a system on chip (SoC)integrated circuit. In at least one embodiment, components of computingsystem 1700 can be integrated into a single package to form a system inpackage (SIP) configuration. In at least one embodiment, at least aportion of components of computing system 1700 can be integrated into amulti-chip module (MCM), which can be interconnected with othermulti-chip modules into a modular computing system.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 17 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one of processor 1702 and parallel processor 1712is used to cause information received from a plurality of 5G new radioantennas to be decoded in parallel by a plurality of processorpipelines. In at least one embodiment, at least one parallel processor1712 is used to layer demap, descramble, and de-rate-match 5G NR PUSCHdata that has been soft demapped in preparation for LDPC decoding, whereat least one thread block is used to perform operations on code blockdata elements (e.g., LLRs) in parallel. In at least one embodiment,processor 1702 executes a kernel launch function that passes parametersto at least one kernel on at least one parallel processor 1712 thatlayer demaps, descrambles, and de-rate matches data from 5G NR antennasin parallel.

Processors

FIG. 18A illustrates a parallel processor 1800 according to at least onembodiment. In at least one embodiment, various components of parallelprocessor 1800 may be implemented using one or more integrated circuitdevices, such as programmable processors, application specificintegrated circuits (ASICs), or field programmable gate arrays (FPGA).In at least one embodiment, illustrated parallel processor 1800 is avariant of one or more parallel processor(s) 1712 shown in FIG. 17according to an exemplary embodiment.

In at least one embodiment, parallel processor 1800 includes a parallelprocessing unit 1802. In at least one embodiment, parallel processingunit 1802 includes an I/O unit 1804 that enables communication withother devices, including other instances of parallel processing unit1802. In at least one embodiment, I/O unit 1804 may be directlyconnected to other devices. In at least one embodiment, I/O unit 1804connects with other devices via use of a hub or switch interface, suchas memory hub 1705. In at least one embodiment, connections betweenmemory hub 1705 and I/O unit 1804 form a communication link 1713. In atleast one embodiment, I/O unit 1804 connects with a host interface 1806and a memory crossbar 1816, where host interface 1806 receives commandsdirected to performing processing operations and memory crossbar 1816receives commands directed to performing memory operations.

In at least one embodiment, when host interface 1806 receives a commandbuffer via I/O unit 1804, host interface 1806 can direct work operationsto perform those commands to a front end 1808. In at least oneembodiment, front end 1808 couples with a scheduler 1810, which isconfigured to distribute commands or other work items to a processingcluster array 1812. In at least one embodiment, scheduler 1810 ensuresthat processing cluster array 1812 is properly configured and in a validstate before tasks are distributed to processing cluster array 1812 ofprocessing cluster array 1812. In at least one embodiment, scheduler1810 is implemented via firmware logic executing on a microcontroller.In at least one embodiment, microcontroller implemented scheduler 1810is configurable to perform complex scheduling and work distributionoperations at coarse and fine granularity, enabling rapid preemption andcontext switching of threads executing on processing array 1812. In atleast one embodiment, host software can prove workloads for schedulingon processing array 1812 via one of multiple graphics processingdoorbells. In at least one embodiment, workloads can then beautomatically distributed across processing array 1812 by scheduler 1810logic within a microcontroller including scheduler 1810.

In at least one embodiment, processing cluster array 1812 can include upto “N” processing clusters (e.g., cluster 1814A, cluster 1814B, throughcluster 1814N). In at least one embodiment, each cluster 1814A-1814N ofprocessing cluster array 1812 can execute a large number of concurrentthreads. In at least one embodiment, scheduler 1810 can allocate work toclusters 1814A-1814N of processing cluster array 1812 using variousscheduling and/or work distribution algorithms, which may vary dependingon workload arising for each type of program or computation. In at leastone embodiment, scheduling can be handled dynamically by scheduler 1810,or can be assisted in part by compiler logic during compilation ofprogram logic configured for execution by processing cluster array 1812.In at least one embodiment, different clusters 1814A-1814N of processingcluster array 1812 can be allocated for processing different types ofprograms or for performing different types of computations.

In at least one embodiment, processing cluster array 1812 can beconfigured to perform various types of parallel processing operations.In at least one embodiment, processing cluster array 1812 is configuredto perform general-purpose parallel compute operations. For example, inat least one embodiment, processing cluster array 1812 can include logicto execute processing tasks including filtering of video and/or audiodata, performing modeling operations, including physics operations, andperforming data transformations.

In at least one embodiment, processing cluster array 1812 is configuredto perform parallel graphics processing operations. In at least oneembodiment, processing cluster array 1812 can include additional logicto support execution of such graphics processing operations, including,but not limited to texture sampling logic to perform texture operations,as well as tessellation logic and other vertex processing logic. In atleast one embodiment, processing cluster array 1812 can be configured toexecute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. In at least one embodiment, parallel processing unit 1802can transfer data from system memory via I/O unit 1804 for processing.In at least one embodiment, during processing, transferred data can bestored to on-chip memory (e.g., parallel processor memory 1822) duringprocessing, then written back to system memory.

In at least one embodiment, when parallel processing unit 1802 is usedto perform graphics processing, scheduler 1810 can be configured todivide a processing workload into approximately equal sized tasks, tobetter enable distribution of graphics processing operations to multipleclusters 1814A-1814N of processing cluster array 1812. In at least oneembodiment, portions of processing cluster array 1812 can be configuredto perform different types of processing. For example, in at least oneembodiment, a first portion may be configured to perform vertex shadingand topology generation, a second portion may be configured to performtessellation and geometry shading, and a third portion may be configuredto perform pixel shading or other screen space operations, to produce arendered image for display. In at least one embodiment, intermediatedata produced by one or more of clusters 1814A-1814N may be stored inbuffers to allow intermediate data to be transmitted between clusters1814A-1814N for further processing.

In at least one embodiment, processing cluster array 1812 can receiveprocessing tasks to be executed via scheduler 1810, which receivescommands defining processing tasks from front end 1808. In at least oneembodiment, processing tasks can include indices of data to beprocessed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howdata is to be processed (e.g., what program is to be executed). In atleast one embodiment, scheduler 1810 may be configured to fetch indicescorresponding to tasks or may receive indices from front end 1808. In atleast one embodiment, front end 1808 can be configured to ensureprocessing cluster array 1812 is configured to a valid state before aworkload specified by incoming command buffers (e.g., batch-buffers,push buffers, etc.) is initiated.

In at least one embodiment, each of one or more instances of parallelprocessing unit 1802 can couple with parallel processor memory 1822. Inat least one embodiment, parallel processor memory 1822 can be accessedvia memory crossbar 1816, which can receive memory requests fromprocessing cluster array 1812 as well as I/O unit 1804. In at least oneembodiment, memory crossbar 1816 can access parallel processor memory1822 via a memory interface 1818. In at least one embodiment, memoryinterface 1818 can include multiple partition units (e.g., partitionunit 1820A, partition unit 1820B, through partition unit 1820N) that caneach couple to a portion (e.g., memory unit) of parallel processormemory 1822. In at least one embodiment, a number of partition units1820A-1820N is configured to be equal to a number of memory units, suchthat a first partition unit 1820A has a corresponding first memory unit1824A, a second partition unit 1820B has a corresponding memory unit1824B, and an Nth partition unit 1820N has a corresponding Nth memoryunit 1824N. In at least one embodiment, a number of partition units1820A-1820N may not be equal to a number of memory devices.

In at least one embodiment, memory units 1824A-1824N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In at least one embodiment, memory units 1824A-1824N may alsoinclude 3D stacked memory, including but not limited to high bandwidthmemory (HBM). In at least one embodiment, render targets, such as framebuffers or texture maps may be stored across memory units 1824A-1824N,allowing partition units 1820A-1820N to write portions of each rendertarget in parallel to efficiently use available bandwidth of parallelprocessor memory 1822. In at least one embodiment, a local instance ofparallel processor memory 1822 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In at least one embodiment, any one of clusters 1814A-1814N ofprocessing cluster array 1812 can process data that will be written toany of memory units 1824A-1824N within parallel processor memory 1822.In at least one embodiment, memory crossbar 1816 can be configured totransfer an output of each cluster 1814A-1814N to any partition unit1820A-1820N or to another cluster 1814A-1814N, which can performadditional processing operations on an output. In at least oneembodiment, each cluster 1814A-1814N can communicate with memoryinterface 1818 through memory crossbar 1816 to read from or write tovarious external memory devices. In at least one embodiment, memorycrossbar 1816 has a connection to memory interface 1818 to communicatewith I/O unit 1804, as well as a connection to a local instance ofparallel processor memory 1822, enabling processing units withindifferent processing clusters 1814A-1814N to communicate with systemmemory or other memory that is not local to parallel processing unit1802. In at least one embodiment, memory crossbar 1816 can use virtualchannels to separate traffic streams between clusters 1814A-1814N andpartition units 1820A-1820N.

In at least one embodiment, multiple instances of parallel processingunit 1802 can be provided on a single add-in card, or multiple add-incards can be interconnected. In at least one embodiment, differentinstances of parallel processing unit 1802 can be configured tointer-operate even if different instances have different numbers ofprocessing cores, different amounts of local parallel processor memory,and/or other configuration differences. For example, in at least oneembodiment, some instances of parallel processing unit 1802 can includehigher precision floating point units relative to other instances. In atleast one embodiment, systems incorporating one or more instances ofparallel processing unit 1802 or parallel processor 1800 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 18B is a block diagram of a partition unit 1820 according to atleast one embodiment. In at least one embodiment, partition unit 1820 isan instance of one of partition units 1820A-1820N of FIG. 18A. In atleast one embodiment, partition unit 1820 includes an L2 cache 1821, aframe buffer interface 1825, and a ROP 1826 (raster operations unit). L2cache 1821 is a read/write cache that is configured to perform load andstore operations received from memory crossbar 1816 and ROP 1826. In atleast one embodiment, read misses and urgent write-back requests areoutput by L2 cache 1821 to frame buffer interface 1825 for processing.In at least one embodiment, updates can also be sent to a frame buffervia frame buffer interface 1825 for processing. In at least oneembodiment, frame buffer interface 1825 interfaces with one of memoryunits in parallel processor memory, such as memory units 1824A-1824N ofFIG. 18 (e.g., within parallel processor memory 1822).

In at least one embodiment, ROP 1826 is a processing unit that performsraster operations such as stencil, z test, blending, and like. In atleast one embodiment, ROP 1826 then outputs processed graphics data thatis stored in graphics memory. In at least one embodiment, ROP 1826includes compression logic to compress depth or color data that iswritten to memory and decompress depth or color data that is read frommemory. In at least one embodiment, compression logic can be losslesscompression logic that makes use of one or more of multiple compressionalgorithms. type of compression that is performed by ROP 1826 can varybased on statistical characteristics of data to be compressed. Forexample, in at least one embodiment, delta color compression isperformed on depth and color data on a per-tile basis.

In In at least one embodiment, ROP 1826 is included within eachprocessing cluster (e.g., cluster 1814A-1814N of FIG. 18) instead ofwithin partition unit 1820. In at least one embodiment, read and writerequests for pixel data are transmitted over memory crossbar 1816instead of pixel fragment data. In at least one embodiment, processedgraphics data may be displayed on a display device, such as one of oneor more display device(s) 1710 of FIG. 17, routed for further processingby processor(s) 1702, or routed for further processing by one ofprocessing entities within parallel processor 1800 of FIG. 18A.

FIG. 18C is a block diagram of a processing cluster 1814 within aparallel processing unit according to at least one embodiment. In atleast one embodiment, a processing cluster is an instance of one ofprocessing clusters 1814A-1814N of FIG. 18. In at least one embodiment,processing cluster 1814 can be configured to execute many threads inparallel, where term “thread” refers to an instance of a particularprogram executing on a particular set of input data. In at least oneembodiment, single-instruction, multiple-data (SIMD) instruction issuetechniques are used to support parallel execution of a large number ofthreads without providing multiple independent instruction units. In atleast one embodiment, single-instruction, multiple-thread (SIMT)techniques are used to support parallel execution of a large number ofgenerally synchronized threads, using a common instruction unitconfigured to issue instructions to a set of processing engines withineach one of processing clusters.

In at least one embodiment, operation of processing cluster 1814 can becontrolled via a pipeline manager 1832 that distributes processing tasksto SIMT parallel processors. In at least one embodiment, pipelinemanager 1832 receives instructions from scheduler 1810 of FIG. 18 andmanages execution of those instructions via a graphics multiprocessor1834 and/or a texture unit 1836. In at least one embodiment, graphicsmultiprocessor 1834 is an exemplary instance of a SIMT parallelprocessor. However, in at least one embodiment, various types of SIMTparallel processors of differing architectures may be included withinprocessing cluster 1814. In at least one embodiment, one or moreinstances of graphics multiprocessor 1834 can be included within aprocessing cluster 1814. In at least one embodiment, graphicsmultiprocessor 1834 can process data and a data crossbar 1840 can beused to distribute processed data to one of multiple possibledestinations, including other shader units. In at least one embodiment,pipeline manager 1832 can facilitate distribution of processed data byspecifying destinations for processed data to be distributed vis datacrossbar 1840.

In at least one embodiment, each graphics multiprocessor 1834 withinprocessing cluster 1814 can include an identical set of functionalexecution logic (e.g., arithmetic logic units, load-store units, etc.).In at least one embodiment, functional execution logic can be configuredin a pipelined manner in which new instructions can be issued beforeprevious instructions are complete. In at least one embodiment,functional execution logic supports a variety of operations includinginteger and floating point arithmetic, comparison operations, Booleanoperations, bit-shifting, and computation of various algebraicfunctions. In at least one embodiment, same functional-unit hardware canbe leveraged to perform different operations and any combination offunctional units may be present.

In at least one embodiment, instructions transmitted to processingcluster 1814 constitute a thread. In at least one embodiment, a set ofthreads executing across a set of parallel processing engines is athread group. In at least one embodiment, thread group executes aprogram on different input data. In at least one embodiment, each threadwithin a thread group can be assigned to a different processing enginewithin a graphics multiprocessor 1834. In at least one embodiment, athread group may include fewer threads than a number of processingengines within graphics multiprocessor 1834. In at least one embodiment,when a thread group includes fewer threads than a number of processingengines, one or more of processing engines may be idle during cycles inwhich that thread group is being processed. In at least one embodiment,a thread group may also include more threads than a number of processingengines within graphics multiprocessor 1834. In at least one embodiment,when a thread group includes more threads than number of processingengines within graphics multiprocessor 1834, processing can be performedover consecutive clock cycles. In at least one embodiment, multiplethread groups can be executed concurrently on a graphics multiprocessor1834.

In at least one embodiment, graphics multiprocessor 1834 includes aninternal cache memory to perform load and store operations. In at leastone embodiment, graphics multiprocessor 1834 can forego an internalcache and use a cache memory (e.g., L1 cache 1848) within processingcluster 1814. In at least one embodiment, each graphics multiprocessor1834 also has access to L2 caches within partition units (e.g.,partition units 1820A-1820N of FIG. 18) that are shared among allprocessing clusters 1814 and may be used to transfer data betweenthreads. In at least one embodiment, graphics multiprocessor 1834 mayalso access off-chip global memory, which can include one or more oflocal parallel processor memory and/or system memory. In at least oneembodiment, any memory external to parallel processing unit 1802 may beused as global memory. In at least one embodiment, processing cluster1814 includes multiple instances of graphics multiprocessor 1834 canshare common instructions and data, which may be stored in L1 cache1848.

In at least one embodiment, each processing cluster 1814 may include anMMU 1845 (memory management unit) that is configured to map virtualaddresses into physical addresses. In at least one embodiment, one ormore instances of MMU 1845 may reside within memory interface 1818 ofFIG. 18. In at least one embodiment, MMU 1845 includes a set of pagetable entries (PTEs) used to map a virtual address to a physical addressof a tile (talk more about tiling) and optionally a cache line index. Inat least one embodiment, MMU 1845 may include address translationlookaside buffers (TLB) or caches that may reside within graphicsmultiprocessor 1834 or L1 cache or processing cluster 1814. In at leastone embodiment, physical address is processed to distribute surface dataaccess locality to allow efficient request interleaving among partitionunits. In at least one embodiment, cache line index may be used todetermine whether a request for a cache line is a hit or miss.

In at least one embodiment, a processing cluster 1814 may be configuredsuch that each graphics multiprocessor 1834 is coupled to a texture unit1836 for performing texture mapping operations, e.g., determiningtexture sample positions, reading texture data, and filtering texturedata. In at least one embodiment, texture data is read from an internaltexture L1 cache (not shown) or from an L1 cache within graphicsmultiprocessor 1834 and is fetched from an L2 cache, local parallelprocessor memory, or system memory, as needed. In at least oneembodiment, each graphics multiprocessor 1834 outputs processed tasks todata crossbar 1840 to provide processed task to another processingcluster 1814 for further processing or to store processed task in an L2cache, local parallel processor memory, or system memory via memorycrossbar 1816. In at least one embodiment, preROP 1842 (pre-rasteroperations unit) is configured to receive data from graphicsmultiprocessor 1834, direct data to ROP units, which may be located withpartition units as described herein (e.g., partition units 1820A-1820Nof FIG. 18). In at least one embodiment, PreROP 1842 unit can performoptimizations for color blending, organize pixel color data, and performaddress translations.

In at least one embodiment, at least one component shown or describedwith respect to FIGS. 18A-C is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one parallel processor 1800 is used to causeinformation received from a plurality of 5G new radio antennas to bedecoded in parallel by a plurality of processor pipelines. In at leastone embodiment, at least one parallel processor 1800 is used to layerdemap, descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel.

FIG. 18D shows a graphics multiprocessor 1834 according to at least oneembodiment. In at least one embodiment, graphics multiprocessor 1834couples with pipeline manager 1832 of processing cluster 1814. In atleast one embodiment, graphics multiprocessor 1834 has an executionpipeline including but not limited to an instruction cache 1852, aninstruction unit 1854, an address mapping unit 1856, a register file1858, one or more general purpose graphics processing unit (GPGPU) cores1862, and one or more load/store units 1866. GPGPU cores 1862 andload/store units 1866 are coupled with cache memory 1872 and sharedmemory 1870 via a memory and cache interconnect 1868.

In at least one embodiment, instruction cache 1852 receives a stream ofinstructions to execute from pipeline manager 1832. In at least oneembodiment, instructions are cached in instruction cache 1852 anddispatched for execution by instruction unit 1854. In at least oneembodiment, instruction unit 1854 can dispatch instructions as threadgroups (e.g., warps), with each thread of thread group assigned to adifferent execution unit within GPGPU core 1862. In at least oneembodiment, an instruction can access any of a local, shared, or globaladdress space by specifying an address within a unified address space.In at least one embodiment, address mapping unit 1856 can be used totranslate addresses in a unified address space into a distinct memoryaddress that can be accessed by load/store units 1866.

In at least one embodiment, register file 1858 provides a set ofregisters for functional units of graphics multiprocessor 1834. In atleast one embodiment, register file 1858 provides temporary storage foroperands connected to data paths of functional units (e.g., GPGPU cores1862, load/store units 1866) of graphics multiprocessor 1834. In atleast one embodiment, register file 1858 is divided between each offunctional units such that each functional unit is allocated a dedicatedportion of register file 1858. In at least one embodiment, register file1858 is divided between different warps being executed by graphicsmultiprocessor 1834.

In at least one embodiment, GPGPU cores 1862 can each include floatingpoint units (FPUs) and/or integer arithmetic logic units (ALUs) that areused to execute instructions of graphics multiprocessor 1834. GPGPUcores 1862 can be similar in architecture or can differ in architecture.In at least one embodiment, a first portion of GPGPU cores 1862 includea single precision FPU and an integer ALU while a second portion ofGPGPU cores include a double precision FPU. In at least one embodiment,FPUs can implement IEEE 754-2008 standard for floating point arithmeticor enable variable precision floating point arithmetic. In at least oneembodiment, graphics multiprocessor 1834 can additionally include one ormore fixed function or special function units to perform specificfunctions such as copy rectangle or pixel blending operations. In atleast one embodiment one or more of GPGPU cores can also include fixedor special function logic.

In at least one embodiment, GPGPU cores 1862 include SIMD logic capableof performing a single instruction on multiple sets of data. In at leastone embodiment GPGPU cores 1862 can physically execute SIMD4, SIMD8, andSIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32instructions. In at least one embodiment, SIMD instructions for GPGPUcores can be generated at compile time by a shader compiler orautomatically generated when executing programs written and compiled forsingle program multiple data (SPMD) or SIMT architectures. In at leastone embodiment, multiple threads of a program configured for an SIMTexecution model can executed via a single SIMD instruction. For example,in at least one embodiment, eight SIMT threads that perform same orsimilar operations can be executed in parallel via a single SIMD8 logicunit.

In at least one embodiment, memory and cache interconnect 1868 is aninterconnect network that connects each functional unit of graphicsmultiprocessor 1834 to register file 1858 and to shared memory 1870. Inat least one embodiment, memory and cache interconnect 1868 is acrossbar interconnect that allows load/store unit 1866 to implement loadand store operations between shared memory 1870 and register file 1858.In at least one embodiment, register file 1858 can operate at a samefrequency as GPGPU cores 1862, thus data transfer between GPGPU cores1862 and register file 1858 is very low latency. In at least oneembodiment, shared memory 1870 can be used to enable communicationbetween threads that execute on functional units within graphicsmultiprocessor 1834. In at least one embodiment, cache memory 1872 canbe used as a data cache for example, to cache texture data communicatedbetween functional units and texture unit 1836. In at least oneembodiment, shared memory 1870 can also be used as a program managedcached. In at least one embodiment, threads executing on GPGPU cores1862 can programmatically store data within shared memory in addition toautomatically cached data that is stored within cache memory 1872.

In at least one embodiment, a parallel processor or GPGPU as describedherein is communicatively coupled to host/processor cores to accelerategraphics operations, machine-learning operations, pattern analysisoperations, and various general purpose GPU (GPGPU) functions. In atleast one embodiment, GPU may be communicatively coupled to hostprocessor/cores over a bus or other interconnect (e.g., a high speedinterconnect such as PCIe or NVLink). In at least one embodiment, GPUmay be integrated on same package or chip as cores and communicativelycoupled to cores over an internal processor bus/interconnect (i.e.,internal to package or chip). In at least one embodiment, regardless ofmanner in which GPU is connected, processor cores may allocate work toGPU in form of sequences of commands/instructions contained in a workdescriptor. In at least one embodiment, GPU then uses dedicatedcircuitry/logic for efficiently processing these commands/instructions.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 18D is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one graphics multiprocessor 1834 is used to causeinformation received from a plurality of 5G new radio antennas to bedecoded in parallel by a plurality of processor pipelines. In at leastone embodiment, at least one graphics multiprocessor 1834 is used tolayer demap, descramble, and de-rate-match 5G NR PUSCH data that hasbeen soft demapped in preparation for LDPC decoding, where at least onethread block is used to perform operations on code block data elements(e.g., LLRs) in parallel.

FIG. 19 illustrates a multi-GPU computing system 1900, according to atleast one embodiment. In at least one embodiment, multi-GPU computingsystem 1900 can include a processor 1902 coupled to multiple generalpurpose graphics processing units (GPGPUs) 1906A-D via a host interfaceswitch 1904. In at least one embodiment, host interface switch 1904 is aPCI express switch device that couples processor 1902 to a PCI expressbus over which processor 1902 can communicate with GPGPUs 1906A-D.GPGPUs 1906A-D can interconnect via a set of high-speed point to pointGPU to GPU links 1916. In at least one embodiment, GPU to GPU links 1916connect to each of GPGPUs 1906A-D via a dedicated GPU link. In at leastone embodiment, P2P GPU links 1916 enable direct communication betweeneach of GPGPUs 1906A-D without requiring communication over hostinterface bus 1904 to which processor 1902 is connected. In at least oneembodiment, with GPU-to-GPU traffic directed to P2P GPU links 1916, hostinterface bus 1904 remains available for system memory access or tocommunicate with other instances of multi-GPU computing system 1900, forexample, via one or more network devices. While in at least oneembodiment GPGPUs 1906A-D connect to processor 1902 via host interfaceswitch 1904, in at least one embodiment processor 1902 includes directsupport for P2P GPU links 1916 and can connect directly to GPGPUs1906A-D.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 19 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one GPGPU 1906 is used to cause informationreceived from a plurality of 5G new radio antennas to be decoded inparallel by a plurality of processor pipelines. In at least oneembodiment, at least one GPGPU 1906 is used to layer demap, descramble,and de-rate-match 5G NR PUSCH data that has been soft demapped inpreparation for LDPC decoding, where at least one thread block is usedto perform operations on code block data elements (e.g., LLRs) inparallel. In at least one embodiment, processor 1902 executes a kernellaunch function that passes parameters to at least one kernel on atleast one GPGPU 1906 that layer demaps, descrambles, and de-rate matchesdata from 5G NR antennas in parallel.

FIG. 20 is a block diagram of a graphics processor 2000, according to atleast one embodiment. In at least one embodiment, graphics processor2000 includes a ring interconnect 2002, a pipeline front-end 2004, amedia engine 2037, and graphics cores 2080A-2080N. In at least oneembodiment, ring interconnect 2002 couples graphics processor 2000 toother processing units, including other graphics processors or one ormore general-purpose processor cores. In at least one embodiment,graphics processor 2000 is one of many processors integrated within amulti-core processing system.

In at least one embodiment, graphics processor 2000 receives batches ofcommands via ring interconnect 2002. In at least one embodiment,incoming commands are interpreted by a command streamer 2003 in pipelinefront-end 2004. In at least one embodiment, graphics processor 2000includes scalable execution logic to perform 3D geometry processing andmedia processing via graphics core(s) 2080A-2080N. In at least oneembodiment, for 3D geometry processing commands, command streamer 2003supplies commands to geometry pipeline 2036. In at least one embodiment,for at least some media processing commands, command streamer 2003supplies commands to a video front end 2034, which couples with a mediaengine 2037. In at least one embodiment, media engine 2037 includes aVideo Quality Engine (VQE) 2030 for video and image post-processing anda multi-format encode/decode (MFX) 2033 engine to providehardware-accelerated media data encode and decode. In at least oneembodiment, geometry pipeline 2036 and media engine 2037 each generateexecution threads for thread execution resources provided by at leastone graphics core 2080A.

In at least one embodiment, graphics processor 2000 includes scalablethread execution resources featuring modular cores 2080A-2080N(sometimes referred to as core slices), each having multiple sub-cores2050A-550N, 2060A-2060N (sometimes referred to as core sub-slices). Inat least one embodiment, graphics processor 2000 can have any number ofgraphics cores 2080A through 2080N. In at least one embodiment, graphicsprocessor 2000 includes a graphics core 2080A having at least a firstsub-core 2050A and a second sub-core 2060A. In at least one embodiment,graphics processor 2000 is a low power processor with a single sub-core(e.g., 2050A). In at least one embodiment, graphics processor 2000includes multiple graphics cores 2080A-2080N, each including a set offirst sub-cores 2050A-2050N and a set of second sub-cores 2060A-2060N.In at least one embodiment, each sub-core in first sub-cores 2050A-2050Nincludes at least a first set of execution units 2052A-2052N andmedia/texture samplers 2054A-2054N. In at least one embodiment, eachsub-core in second sub-cores 2060A-2060N includes at least a second setof execution units 2062A-2062N and samplers 2064A-2064N. In at least oneembodiment, each sub-core 2050A-2050N, 2060A-2060N shares a set ofshared resources 2070A-2070N. In at least one embodiment, sharedresources include shared cache memory and pixel operation logic.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 20 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one graphics processor 2000 is used to causeinformation received from a plurality of 5G new radio antennas to bedecoded in parallel by a plurality of processor pipelines. In at leastone embodiment, at least one graphics processor 2000 is used to layerdemap, descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel.

FIG. 21 is a block diagram illustrating micro-architecture for aprocessor 2100 that may include logic circuits to perform instructions,according to at least one embodiment. In at least one embodiment,processor 2100 may perform instructions, including x86 instructions, ARMinstructions, specialized instructions for application-specificintegrated circuits (ASICs), etc. In at least one embodiment, processor2110 may include registers to store packed data, such as 64-bit wideMMX™ registers in microprocessors enabled with MMX technology from IntelCorporation of Santa Clara, Calif. In at least one embodiment, MMXregisters, available in both integer and floating point forms, mayoperate with packed data elements that accompany single instruction,multiple data (“SIMD”) and streaming SIMD extensions (“SSE”)instructions. In at least one embodiment, 128-bit wide XMM registersrelating to SSE2, SSE3, SSE4, AVX, or beyond (referred to generically as“SSEx”) technology may hold such packed data operands. In at least oneembodiment, processors 2110 may perform instructions to acceleratemachine learning or deep learning algorithms, training, or inferencing.

In at least one embodiment, processor 2100 includes an in-order frontend (“front end”) 2101 to fetch instructions to be executed and prepareinstructions to be used later in processor pipeline. In at least oneembodiment, front end 2101 may include several units. In at least oneembodiment, an instruction prefetcher 2126 fetches instructions frommemory and feeds instructions to an instruction decoder 2128 which inturn decodes or interprets instructions. For example, in at least oneembodiment, instruction decoder 2128 decodes a received instruction intoone or more operations called “micro-instructions” or “micro-operations”(also called “micro ops” or “uops”) that machine may execute. In atleast one embodiment, instruction decoder 2128 parses instruction intoan opcode and corresponding data and control fields that may be used bymicro-architecture to perform operations in accordance with at least oneembodiment. In at least one embodiment, a trace cache 2130 may assembledecoded uops into program ordered sequences or traces in a uop queue2134 for execution. In at least one embodiment, when trace cache 2130encounters a complex instruction, a microcode ROM 2132 provides uopsneeded to complete operation.

In at least one embodiment, some instructions may be converted into asingle micro-op, whereas others need several micro-ops to complete fulloperation. In at least one embodiment, if more than four micro-ops areneeded to complete an instruction, instruction decoder 2128 may accessmicrocode ROM 2132 to perform instruction. In at least one embodiment,an instruction may be decoded into a small number of micro-ops forprocessing at instruction decoder 2128. In at least one embodiment, aninstruction may be stored within microcode ROM 2132 should a number ofmicro-ops be needed to accomplish operation. In at least one embodiment,trace cache 2130 refers to an entry point programmable logic array(“PLA”) to determine a correct micro-instruction pointer for readingmicrocode sequences to complete one or more instructions from microcodeROM 2132 in accordance with at least one embodiment. In at least oneembodiment, after microcode ROM 2132 finishes sequencing micro-ops foran instruction, front end 2101 of machine may resume fetching micro-opsfrom trace cache 2130.

In at least one embodiment, out-of-order execution engine (“out of orderengine”) 2103 may prepare instructions for execution. In at least oneembodiment, out-of-order execution logic has a number of buffers tosmooth out and re-order flow of instructions to optimize performance asthey go down pipeline and get scheduled for execution. out-of-orderexecution engine 2103 includes, without limitation, anallocator/register renamer 2140, a memory uop queue 2142, aninteger/floating point uop queue 2144, a memory scheduler 2146, a fastscheduler 2102, a slow/general floating point scheduler (“slow/generalFP scheduler”) 2104, and a simple floating point scheduler (“simple FPscheduler”) 2106. In at least one embodiment, fast schedule 2102,slow/general floating point scheduler 2104, and simple floating pointscheduler 2106 are also collectively referred to herein as “uopschedulers 2102, 2104, 2106.” allocator/register renamer 2140 allocatesmachine buffers and resources that each uop needs in order to execute.In at least one embodiment, allocator/register renamer 2140 renameslogic registers onto entries in a register file. In at least oneembodiment, allocator/register renamer 2140 also allocates an entry foreach uop in one of two uop queues, memory uop queue 2142 for memoryoperations and integer/floating point uop queue 2144 for non-memoryoperations, in front of memory scheduler 2146 and uop schedulers 2102,2104, 2106. In at least one embodiment, uop schedulers 2102, 2104, 2106,determine when a uop is ready to execute based on readiness of theirdependent input register operand sources and availability of executionresources uops need to complete their operation. In at least oneembodiment, fast scheduler 2102 of at least one embodiment may scheduleon each half of main clock cycle while slow/general floating pointscheduler 2104 and simple floating point scheduler 2106 may scheduleonce per main processor clock cycle. In at least one embodiment, uopschedulers 2102, 2104, 2106 arbitrate for dispatch ports to scheduleuops for execution.

In at least one embodiment, execution block b11 includes, withoutlimitation, an integer register file/bypass network 2108, a floatingpoint register file/bypass network (“FP register file/bypass network”)2110, address generation units (“AGUs”) 2112 and 2114, fast ArithmeticLogic Units (ALUs) (“fast ALUs”) 2116 and 2118, a slow Arithmetic LogicUnit (“slow ALU”) 2120, a floating point ALU (“FP”) 2122, and a floatingpoint move unit (“FP move”) 2124. In at least one embodiment, integerregister file/bypass network 2108 and floating point registerfile/bypass network 2110 are also referred to herein as “register files2108, 2110.” In at least one embodiment, AGUSs 2112 and 2114, fast ALUs2116 and 2118, slow ALU 2120, floating point ALU 2122, and floatingpoint move unit 2124 are also referred to herein as “execution units2112, 2114, 2116, 2118, 2120, 2122, and 2124.” In at least oneembodiment, execution block b11 may include, without limitation, anynumber (including zero) and type of register files, bypass networks,address generation units, and execution units, in any combination.

In at least one embodiment, register files 2108, 2110 may be arrangedbetween uop schedulers 2102, 2104, 2106, and execution units 2112, 2114,2116, 2118, 2120, 2122, and 2124. In at least one embodiment, integerregister file/bypass network 2108 performs integer operations. In atleast one embodiment, floating point register file/bypass network 2110performs floating point operations. In at least one embodiment, each ofregister files 2108, 2110 may include, without limitation, a bypassnetwork that may bypass or forward just completed results that have notyet been written into register file to new dependent uops. In at leastone embodiment, register files 2108, 2110 may communicate data with eachother. In at least one embodiment, integer register file/bypass network2108 may include, without limitation, two separate register files, oneregister file for low-order thirty-two bits of data and a secondregister file for high order thirty-two bits of data. In at least oneembodiment, floating point register file/bypass network 2110 mayinclude, without limitation, 128-bit wide entries because floating pointinstructions typically have operands from 64 to 128 bits in width.

In at least one embodiment, execution units 2112, 2114, 2116, 2118,2120, 2122, 2124 may execute instructions. In at least one embodiment,register files 2108, 2110 store integer and floating point data operandvalues that micro-instructions need to execute. In at least oneembodiment, processor 2100 may include, without limitation, any numberand combination of execution units 2112, 2114, 2116, 2118, 2120, 2122,2124. In at least one embodiment, floating point ALU 2122 and floatingpoint move unit 2124, may execute floating point, MMX, SIMD, AVX andSSE, or other operations, including specialized machine learninginstructions. In at least one embodiment, floating point ALU 2122 mayinclude, without limitation, a 64-bit by 64-bit floating point dividerto execute divide, square root, and remainder micro ops. In at least oneembodiment, instructions involving a floating point value may be handledwith floating point hardware. In at least one embodiment, ALU operationsmay be passed to fast ALUs 2116, 2118. In at least one embodiment, fastALUS 2116, 2118 may execute fast operations with an effective latency ofhalf a clock cycle. In at least one embodiment, most complex integeroperations go to slow ALU 2120 as slow ALU 2120 may include, withoutlimitation, integer execution hardware for long-latency type ofoperations, such as a multiplier, shifts, flag logic, and branchprocessing. In at least one embodiment, memory load/store operations maybe executed by AGUS 2112, 2114. In at least one embodiment, fast ALU2116, fast ALU 2118, and slow ALU 2120 may perform integer operations on64-bit data operands. In at least one embodiment, fast ALU 2116, fastALU 2118, and slow ALU 2120 may be implemented to support a variety ofdata bit sizes including sixteen, thirty-two, 128, 256, etc. In at leastone embodiment, floating point ALU 2122 and floating point move unit2124 may be implemented to support a range of operands having bits ofvarious widths. In at least one embodiment, floating point ALU 2122 andfloating point move unit 2124 may operate on 128-bit wide packed dataoperands in conjunction with SIMD and multimedia instructions.

In at least one embodiment, uop schedulers 2102, 2104, 2106, dispatchdependent operations before parent load has finished executing. In atleast one embodiment, as uops may be speculatively scheduled andexecuted in processor 2100, processor 2100 may also include logic tohandle memory misses. In at least one embodiment, if a data load missesin data cache, there may be dependent operations in flight in pipelinethat have left scheduler with temporarily incorrect data. In at leastone embodiment, a replay mechanism tracks and re-executes instructionsthat use incorrect data. In at least one embodiment, dependentoperations might need to be replayed and independent ones may be allowedto complete. In at least one embodiment, schedulers and replay mechanismof at least one embodiment of a processor may also be designed to catchinstruction sequences for text string comparison operations.

In at least one embodiment, term “registers” may refer to on-boardprocessor storage locations that may be used as part of instructions toidentify operands. In at least one embodiment, registers may be thosethat may be usable from outside of processor (from a programmer'sperspective). In at least one embodiment, registers might not be limitedto a particular type of circuit. Rather, in at least one embodiment, aregister may store data, provide data, and perform functions describedherein. In at least one embodiment, registers described herein may beimplemented by circuitry within a processor using any number ofdifferent techniques, such as dedicated physical registers, dynamicallyallocated physical registers using register renaming, combinations ofdedicated and dynamically allocated physical registers, etc. In at leastone embodiment, integer registers store 32-bit integer data. A registerfile of at least one embodiment also contains eight multimedia SIMDregisters for packed data.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 21 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one processor 2100 is used to cause informationreceived from a plurality of 5G new radio antennas to be decoded inparallel by a plurality of processor pipelines. In at least oneembodiment, at least one processor 2100 is used to layer demap,descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel.

FIG. 22 is a block diagram of a processing system, according to at leastone embodiment. In at least one embodiment, system 2200 includes one ormore processors 2202 and one or more graphics processors 2208, and maybe a single processor desktop system, a multiprocessor workstationsystem, or a server system having a large number of processors 2202 orprocessor cores 2207. In at least one embodiment, system 2200 is aprocessing platform incorporated within a system-on-a-chip (SoC)integrated circuit for use in mobile, handheld, or embedded devices.

In at least one embodiment, system 2200 can include, or be incorporatedwithin a server-based gaming platform, a game console, including a gameand media console, a mobile gaming console, a handheld game console, oran online game console. In at least one embodiment, system 2200 is amobile phone, smart phone, tablet computing device or mobile Internetdevice. In at least one embodiment, processing system 2200 can alsoinclude, couple with, or be integrated within a wearable device, such asa smart watch wearable device, smart eyewear device, augmented realitydevice, or virtual reality device. In at least one embodiment,processing system 2200 is a television or set top box device having oneor more processors 2202 and a graphical interface generated by one ormore graphics processors 2208.

In at least one embodiment, one or more processors 2202 each include oneor more processor cores 2207 to process instructions which, whenexecuted, perform operations for system and user software. In at leastone embodiment, each of one or more processor cores 2207 is configuredto process a specific instruction set 2209. In at least one embodiment,instruction set 2209 may facilitate Complex Instruction Set Computing(CISC), Reduced Instruction Set Computing (RISC), or computing via aVery Long Instruction Word (VLIW). In at least one embodiment, processorcores 2207 may each process a different instruction set 2209, which mayinclude instructions to facilitate emulation of other instruction sets.In at least one embodiment, processor core 2207 may also include otherprocessing devices, such a Digital Signal Processor (DSP).

In at least one embodiment, processor 2202 includes cache memory 2204.In at least one embodiment, processor 2202 can have a single internalcache or multiple levels of internal cache. In at least one embodiment,cache memory is shared among various components of processor 2202. In atleast one embodiment, processor 2202 also uses an external cache (e.g.,a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which maybe shared among processor cores 2207 using known cache coherencytechniques. In at least one embodiment, register file 2206 isadditionally included in processor 2202 which may include differenttypes of registers for storing different types of data (e.g., integerregisters, floating point registers, status registers, and aninstruction pointer register). In at least one embodiment, register file2206 may include general-purpose registers or other registers.

In at least one embodiment, one or more processor(s) 2202 are coupledwith one or more interface bus(es) 2210 to transmit communicationsignals such as address, data, or control signals between processor 2202and other components in system 2200. In at least one embodiment,interface bus 2210, in one embodiment, can be a processor bus, such as aversion of a Direct Media Interface (DMI) bus. In at least oneembodiment, interface 2210 is not limited to a DMI bus, and may includeone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress), memory busses, or other types of interface busses. In at leastone embodiment, processor(s) 2202 include an integrated memorycontroller 2216 and a platform controller hub 2230. In at least oneembodiment, memory controller 2216 facilitates communication between amemory device and other components of system 2200, while platformcontroller hub (PCH) 2230 provides connections to I/O devices via alocal I/O bus.

In at least one embodiment, memory device 2220 can be a dynamic randomaccess memory (DRAM) device, a static random access memory (SRAM)device, flash memory device, phase-change memory device, or some othermemory device having suitable performance to serve as process memory. Inat least one embodiment memory device 2220 can operate as system memoryfor system 2200, to store data 2222 and instructions 2221 for use whenone or more processors 2202 executes an application or process. In atleast one embodiment, memory controller 2216 also couples with anoptional external graphics processor 2212, which may communicate withone or more graphics processors 2208 in processors 2202 to performgraphics and media operations. In at least one embodiment, a displaydevice 2211 can connect to processor(s) 2202. In at least one embodimentdisplay device 2211 can include one or more of an internal displaydevice, as in a mobile electronic device or a laptop device or anexternal display device attached via a display interface (e.g.,DisplayPort, etc.). In at least one embodiment, display device 2211 caninclude a head mounted display (HMD) such as a stereoscopic displaydevice for use in virtual reality (VR) applications or augmented reality(AR) applications.

In at least one embodiment, platform controller hub 2230 enablesperipherals to connect to memory device 2220 and processor 2202 via ahigh-speed I/O bus. In at least one embodiment, I/O peripherals include,but are not limited to, an audio controller 2246, a network controller2234, a firmware interface 2228, a wireless transceiver 2226, touchsensors 2225, a data storage device 2224 (e.g., hard disk drive, flashmemory, etc.). In at least one embodiment, data storage device 2224 canconnect via a storage interface (e.g., SATA) or via a peripheral bus,such as a Peripheral Component Interconnect bus (e.g., PCI, PCIExpress). In at least one embodiment, touch sensors 2225 can includetouch screen sensors, pressure sensors, or fingerprint sensors. In atleast one embodiment, wireless transceiver 2226 can be a Wi-Fitransceiver, a Bluetooth transceiver, or a mobile network transceiversuch as a 3G, 4G, or Long Term Evolution (LTE) transceiver. In at leastone embodiment, firmware interface 2228 enables communication withsystem firmware, and can be, for example, a unified extensible firmwareinterface (UEFI). In at least one embodiment, network controller 2234can enable a network connection to a wired network. In at least oneembodiment, a high-performance network controller (not shown) coupleswith interface bus 2210. In at least one embodiment, audio controller2246 is a multi-channel high definition audio controller. In at leastone embodiment, system 2200 includes an optional legacy I/O controller2240 for coupling legacy (e.g., Personal System 2 (PS/2)) devices tosystem. In at least one embodiment, platform controller hub 2230 canalso connect to one or more Universal Serial Bus (USB) controllers 2242connect input devices, such as keyboard and mouse 2243 combinations, acamera 2244, or other USB input devices.

In at least one embodiment, an instance of memory controller 2216 andplatform controller hub 2230 may be integrated into a discreet externalgraphics processor, such as external graphics processor 2212. In atleast one embodiment, platform controller hub 2230 and/or memorycontroller 2216 may be external to one or more processor(s) 2202. Forexample, in at least one embodiment, system 2200 can include an externalmemory controller 2216 and platform controller hub 2230, which may beconfigured as a memory controller hub and peripheral controller hubwithin a system chipset that is in communication with processor(s) 2202.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 22 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one graphics processor 2208 is used to causeinformation received from a plurality of 5G new radio antennas to bedecoded in parallel by a plurality of processor pipelines. In at leastone embodiment, at least one graphics processor 2208 is used to layerdemap, descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel. In at least one embodiment, processor core 2207executes a kernel launch function that passes parameters to at least onekernel on at least one graphics processor 2208 that layer demaps,descrambles, and de-rate matches data from 5G NR antennas in parallel.

FIG. 23 is a block diagram of a processor 2300 having one or moreprocessor cores 2302A-2302N, an integrated memory controller 2314, andan integrated graphics processor 2308, according to at least oneembodiment. In at least one embodiment, processor 2300 can includeadditional cores up to and including additional core 2302N representedby dashed lined boxes. In at least one embodiment, each of processorcores 2302A-2302N includes one or more internal cache units 2304A-2304N.In at least one embodiment, each processor core also has access to oneor more shared cached units 2306.

In at least one embodiment, internal cache units 2304A-2304N and sharedcache units 2306 represent a cache memory hierarchy within processor2300. In at least one embodiment, cache memory units 2304A-2304N mayinclude at least one level of instruction and data cache within eachprocessor core and one or more levels of shared mid-level cache, such asa Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache,where a highest level of cache before external memory is classified asan LLC. In at least one embodiment, cache coherency logic maintainscoherency between various cache units 2306 and 2304A-2304N.

In at least one embodiment, processor 2300 may also include a set of oneor more bus controller units 2316 and a system agent core 2310. In atleast one embodiment, one or more bus controller units 2316 manage a setof peripheral buses, such as one or more PCI or PCI express buses. In atleast one embodiment, system agent core 2310 provides managementfunctionality for various processor components. In at least oneembodiment, system agent core 2310 includes one or more integratedmemory controllers 2314 to manage access to various external memorydevices (not shown).

In at least one embodiment, one or more of processor cores 2302A-2302Ninclude support for simultaneous multi-threading. In at least oneembodiment, system agent core 2310 includes components for coordinatingand operating cores 2302A-2302N during multi-threaded processing. In atleast one embodiment, system agent core 2310 may additionally include apower control unit (PCU), which includes logic and components toregulate one or more power states of processor cores 2302A-2302N andgraphics processor 2308.

In at least one embodiment, processor 2300 additionally includesgraphics processor 2308 to execute graphics processing operations. In atleast one embodiment, graphics processor 2308 couples with shared cacheunits 2306, and system agent core 2310, including one or more integratedmemory controllers 2314. In at least one embodiment, system agent core2310 also includes a display controller 2311 to drive graphics processoroutput to one or more coupled displays. In at least one embodiment,display controller 2311 may also be a separate module coupled withgraphics processor 2308 via at least one interconnect, or may beintegrated within graphics processor 2308.

In at least one embodiment, a ring based interconnect unit 2312 is usedto couple internal components of processor 2300. In at least oneembodiment, an alternative interconnect unit may be used, such as apoint-to-point interconnect, a switched interconnect, or othertechniques. In at least one embodiment, graphics processor 2308 coupleswith ring interconnect 2312 via an I/O link 2313.

In at least one embodiment, I/O link 2313 represents at least one ofmultiple varieties of I/O interconnects, including an on package I/Ointerconnect which facilitates communication between various processorcomponents and a high-performance embedded memory module 2318, such asan eDRAM module. In at least one embodiment, each of processor cores2302A-2302N and graphics processor 2308 use embedded memory modules 2318as a shared Last Level Cache.

In at least one embodiment, processor cores 2302A-2302N are homogenouscores executing a common instruction set architecture. In at least oneembodiment, processor cores 2302A-2302N are heterogeneous in terms ofinstruction set architecture (ISA), where one or more of processor cores2302A-2302N execute a common instruction set, while one or more othercores of processor cores 2302A-23-02N executes a subset of a commoninstruction set or a different instruction set. In at least oneembodiment, processor cores 2302A-2302N are heterogeneous in terms ofmicroarchitecture, where one or more cores having a relatively higherpower consumption couple with one or more power cores having a lowerpower consumption. In at least one embodiment, processor 2300 can beimplemented on one or more chips or as an SoC integrated circuit.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 23 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one graphics processor 2308 is used to causeinformation received from a plurality of 5G new radio antennas to bedecoded in parallel by a plurality of processor pipelines. In at leastone embodiment, at least one graphics processor 2308 is used to layerdemap, descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel. In at least one embodiment, at least one processorcore 2302 executes a kernel launch function that passes parameters to atleast one kernel on at least one graphics processor 2308 that layerdemaps, descrambles, and de-rate matches data from 5G NR antennas inparallel.

FIG. 24 is a block diagram of a graphics processor 2400, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In at least oneembodiment, graphics processor 2400 communicates via a memory mapped I/Ointerface to registers on graphics processor 2400 and with commandsplaced into memory. In at least one embodiment, graphics processor 2400includes a memory interface 2414 to access memory. In at least oneembodiment, memory interface 2414 is an interface to local memory, oneor more internal caches, one or more shared external caches, and/or tosystem memory.

In at least one embodiment, graphics processor 2400 also includes adisplay controller 2402 to drive display output data to a display device2420. In at least one embodiment, display controller 2402 includeshardware for one or more overlay planes for display device 2420 andcomposition of multiple layers of video or user interface elements. Inat least one embodiment, display device 2420 can be an internal orexternal display device. In at least one embodiment, display device 2420is a head mounted display device, such as a virtual reality (VR) displaydevice or an augmented reality (AR) display device. In at least oneembodiment, graphics processor 2400 includes a video codec engine 2406to encode, decode, or transcode media to, from, or between one or moremedia encoding formats, including, but not limited to Moving PictureExperts Group (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC)formats such as H.264/MPEG-4 AVC, as well as Society of Motion Picture &Television Engineers (SMPTE) 421M/VC-1, and Joint Photographic ExpertsGroup (JPEG) formats such as JPEG, and Motion JPEG (MJPEG) formats.

In at least one embodiment, graphics processor 2400 includes a blockimage transfer (BLIT) engine 2404 to perform two-dimensional (2D)rasterizer operations including, for example, bit-boundary blocktransfers. However, in at least one embodiment, 2D graphics operationsare performed using one or more components of graphics processing engine(GPE) 2410. In at least one embodiment, GPE 2410 is a compute engine forperforming graphics operations, including three-dimensional (3D)graphics operations and media operations.

In at least one embodiment, GPE 2410 includes a 3D pipeline 2412 forperforming 3D operations, such as rendering three-dimensional images andscenes using processing functions that act upon 3D primitive shapes(e.g., rectangle, triangle, etc.). 3D pipeline 2412 includesprogrammable and fixed function elements that perform various tasksand/or spawn execution threads to a 3D/Media sub-system 2415. While 3Dpipeline 2412 can be used to perform media operations, in at least oneembodiment, GPE 2410 also includes a media pipeline 2416 that is used toperform media operations, such as video post-processing and imageenhancement.

In at least one embodiment, media pipeline 2416 includes fixed functionor programmable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 2406. In at least one embodiment, media pipeline 2416additionally includes a thread spawning unit to spawn threads forexecution on 3D/Media sub-system 2415. In at least one embodiment,spawned threads perform computations for media operations on one or moregraphics execution units included in 3D/Media sub-system 2415.

In at least one embodiment, 3D/Media subsystem 2415 includes logic forexecuting threads spawned by 3D pipeline 2412 and media pipeline 2416.In at least one embodiment, 3D pipeline 2412 and media pipeline 2416send thread execution requests to 3D/Media subsystem 2415, whichincludes thread dispatch logic for arbitrating and dispatching variousrequests to available thread execution resources. In at least oneembodiment, execution resources include an array of graphics executionunits to process 3D and media threads. In at least one embodiment,3D/Media subsystem 2415 includes one or more internal caches for threadinstructions and data. In at least one embodiment, subsystem 2415 alsoincludes shared memory, including registers and addressable memory, toshare data between threads and to store output data.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 24 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one graphics processor 2400 is used to causeinformation received from a plurality of 5G new radio antennas to bedecoded in parallel by a plurality of processor pipelines. In at leastone embodiment, at least one graphics processor 2400 is used to layerdemap, descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel.

FIG. 25 is a block diagram of a graphics processing engine 2510 of agraphics processor in accordance with at least one embodiment. In atleast one embodiment, graphics processing engine (GPE) 2510 is a versionof GPE 2410 shown in FIG. 24. In at least one embodiment, media pipeline2516 is optional and may not be explicitly included within GPE 2510. Inat least one embodiment, a separate media and/or image processor iscoupled to GPE 2510.

In at least one embodiment, GPE 2510 is coupled to or includes a commandstreamer 2503, which provides a command stream to 3D pipeline 2512and/or media pipelines 2516. In at least one embodiment, commandstreamer 2503 is coupled to memory, which can be system memory, or oneor more of internal cache memory and shared cache memory. In at leastone embodiment, command streamer 2503 receives commands from memory andsends commands to 3D pipeline 2512 and/or media pipeline 2516. In atleast one embodiment, commands are instructions, primitives, ormicro-operations fetched from a ring buffer, which stores commands for3D pipeline 2512 and media pipeline 2516. In at least one embodiment, aring buffer can additionally include batch command buffers storingbatches of multiple commands. In at least one embodiment, commands for3D pipeline 2512 can also include references to data stored in memory,such as but not limited to vertex and geometry data for 3D pipeline 2512and/or image data and memory objects for media pipeline 2516. In atleast one embodiment, 3D pipeline 2512 and media pipeline 2516 processcommands and data by performing operations or by dispatching one or moreexecution threads to a graphics core array 2514. In at least oneembodiment graphics core array 2514 includes one or more blocks ofgraphics cores (e.g., graphics core(s) 2515A, graphics core(s) 2515B),each block including one or more graphics cores. In at least oneembodiment, each graphics core includes a set of graphics executionresources that includes general-purpose and graphics specific executionlogic to perform graphics and compute operations, as well as fixedfunction texture processing and/or machine learning and artificialintelligence acceleration logic.

In at least one embodiment, 3D pipeline 2512 includes fixed function andprogrammable logic to process one or more shader programs, such asvertex shaders, geometry shaders, pixel shaders, fragment shaders,compute shaders, or other shader programs, by processing instructionsand dispatching execution threads to graphics core array 2514. In atleast one embodiment, graphics core array 2514 provides a unified blockof execution resources for use in processing shader programs. In atleast one embodiment, multi-purpose execution logic (e.g., executionunits) within graphics core(s) 2515A-2515B of graphic core array 2514includes support for various 3D API shader languages and can executemultiple simultaneous execution threads associated with multipleshaders.

In at least one embodiment, graphics core array 2514 also includesexecution logic to perform media functions, such as video and/or imageprocessing. In at least one embodiment, execution units additionallyinclude general-purpose logic that is programmable to perform parallelgeneral-purpose computational operations, in addition to graphicsprocessing operations.

In at least one embodiment, output data generated by threads executingon graphics core array 2514 can output data to memory in a unifiedreturn buffer (URB) 2518. URB 2518 can store data for multiple threads.In at least one embodiment, URB 2518 may be used to send data betweendifferent threads executing on graphics core array 2514. In at least oneembodiment, URB 2518 may additionally be used for synchronizationbetween threads on graphics core array 2514 and fixed function logicwithin shared function logic 2520.

In at least one embodiment, graphics core array 2514 is scalable, suchthat graphics core array 2514 includes a variable number of graphicscores, each having a variable number of execution units based on atarget power and performance level of GPE 2510. In at least oneembodiment, execution resources are dynamically scalable, such thatexecution resources may be enabled or disabled as needed.

In at least one embodiment, graphics core array 2514 is coupled toshared function logic 2520 that includes multiple resources that areshared between graphics cores in graphics core array 2514. In at leastone embodiment, shared functions performed by shared function logic 2520are embodied in hardware logic units that provide specializedsupplemental functionality to graphics core array 2514. In at least oneembodiment, shared function logic 2520 includes but is not limited tosampler 2521, math 2522, and inter-thread communication (ITC) 2523logic. In at least one embodiment, one or more cache(s) 2525 are inincluded in or couple to shared function logic 2520.

In at least one embodiment, a shared function is used if demand for aspecialized function is insufficient for inclusion within graphics corearray 2514. In at least one embodiment, a single instantiation of aspecialized function is used in shared function logic 2520 and sharedamong other execution resources within graphics core array 2514. In atleast one embodiment, specific shared functions within shared functionlogic 2520 that are used extensively by graphics core array 2514 may beincluded within shared function logic 2516 within graphics core array2514. In at least one embodiment, shared function logic 2516 withingraphics core array 2514 can include some or all logic within sharedfunction logic 2520. In at least one embodiment, all logic elementswithin shared function logic 2520 may be duplicated within sharedfunction logic 2516 of graphics core array 2514. In at least oneembodiment, shared function logic 2520 is excluded in favor of sharedfunction logic 2516 within graphics core array 2514.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 25 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one graphics processing engine 2510 is used tocause information received from a plurality of 5G new radio antennas tobe decoded in parallel by a plurality of processor pipelines. In atleast one embodiment, at least one graphics processing engine 2510 isused to layer demap, descramble, and de-rate-match 5G NR PUSCH data thathas been soft demapped in preparation for LDPC decoding, where at leastone thread block is used to perform operations on code block dataelements (e.g., LLRs) in parallel.

FIG. 26 is a block diagram of hardware logic of a graphics processorcore 2600, according to at least one embodiment described herein. In atleast one embodiment, graphics processor core 2600 is included within agraphics core array. In at least one embodiment, graphics processor core2600, sometimes referred to as a core slice, can be one or multiplegraphics cores within a modular graphics processor. In at least oneembodiment, graphics processor core 2600 is exemplary of one graphicscore slice, and a graphics processor as described herein may includemultiple graphics core slices based on target power and performanceenvelopes. In at least one embodiment, each graphics core 2600 caninclude a fixed function block 2630 coupled with multiple sub-cores2601A-2601F, also referred to as sub-slices, that include modular blocksof general-purpose and fixed function logic.

In at least one embodiment, fixed function block 2630 includes ageometry/fixed function pipeline 2636 that can be shared by allsub-cores in graphics processor 2600, for example, in lower performanceand/or lower power graphics processor implementations. In at least oneembodiment, geometry/fixed function pipeline 2636 includes a 3D fixedfunction pipeline, a video front-end unit, a thread spawner and threaddispatcher, and a unified return buffer manager, which manages unifiedreturn buffers.

In at least one embodiment fixed function block 2630 also includes agraphics SoC interface 2637, a graphics microcontroller 2638, and amedia pipeline 2639. Graphics SoC interface 2637 provides an interfacebetween graphics core 2600 and other processor cores within a system ona chip integrated circuit. In at least one embodiment, graphicsmicrocontroller 2638 is a programmable sub-processor that isconfigurable to manage various functions of graphics processor 2600,including thread dispatch, scheduling, and pre-emption. In at least oneembodiment, media pipeline 2639 includes logic to facilitate decoding,encoding, pre-processing, and/or post-processing of multimedia data,including image and video data. In at least one embodiment, mediapipeline 2639 implement media operations via requests to compute orsampling logic within sub-cores 2601-2601F.

In at least one embodiment, SoC interface 2637 enables graphics core2600 to communicate with general-purpose application processor cores(e.g., CPUs) and/or other components within an SoC, including memoryhierarchy elements such as a shared last level cache memory, system RAM,and/or embedded on-chip or on-package DRAM. In at least one embodiment,SoC interface 2637 can also enable communication with fixed functiondevices within an SoC, such as camera imaging pipelines, and enables useof and/or implements global memory atomics that may be shared betweengraphics core 2600 and CPUs within an SoC. In at least one embodiment,SoC interface 2637 can also implement power management controls forgraphics core 2600 and enable an interface between a clock domain ofgraphic core 2600 and other clock domains within an SoC. In at least oneembodiment, SoC interface 2637 enables receipt of command buffers from acommand streamer and global thread dispatcher that are configured toprovide commands and instructions to each of one or more graphics coreswithin a graphics processor. In at least one embodiment, commands andinstructions can be dispatched to media pipeline 2639, when mediaoperations are to be performed, or a geometry and fixed functionpipeline (e.g., geometry and fixed function pipeline 2636, geometry andfixed function pipeline 2614) when graphics processing operations are tobe performed.

In at least one embodiment, graphics microcontroller 2638 can beconfigured to perform various scheduling and management tasks forgraphics core 2600. In at least one embodiment, graphics microcontroller2638 can perform graphics and/or compute workload scheduling on variousgraphics parallel engines within execution unit (EU) arrays 2602A-2602F,2604A-2604F within sub-cores 2601A-2601F. In at least one embodiment,host software executing on a CPU core of an SoC including graphics core2600 can submit workloads one of multiple graphic processor doorbells,which invokes a scheduling operation on an appropriate graphics engine.In at least one embodiment, scheduling operations include determiningwhich workload to run next, submitting a workload to a command streamer,pre-empting existing workloads running on an engine, monitoring progressof a workload, and notifying host software when a workload is complete.In at least one embodiment, graphics microcontroller 2638 can alsofacilitate low-power or idle states for graphics core 2600, providinggraphics core 2600 with an ability to save and restore registers withingraphics core 2600 across low-power state transitions independently froman operating system and/or graphics driver software on a system.

In at least one embodiment, graphics core 2600 may have greater than orfewer than illustrated sub-cores 2601A-2601F, up to N modular sub-cores.For each set of N sub-cores, in at least one embodiment, graphics core2600 can also include shared function logic 2610, shared and/or cachememory 2612, a geometry/fixed function pipeline 2614, as well asadditional fixed function logic 2616 to accelerate various graphics andcompute processing operations. In at least one embodiment, sharedfunction logic 2610 can include logic units (e.g., sampler, math, and/orinter-thread communication logic) that can be shared by each N sub-coreswithin graphics core 2600. Shared and/or cache memory 2612 can be alast-level cache for N sub-cores 2601A-2601F within graphics core 2600and can also serve as shared memory that is accessible by multiplesub-cores. In at least one embodiment, geometry/fixed function pipeline2614 can be included instead of geometry/fixed function pipeline 2636within fixed function block 2630 and can include same or similar logicunits.

In at least one embodiment, graphics core 2600 includes additional fixedfunction logic 2616 that can include various fixed function accelerationlogic for use by graphics core 2600. In at least one embodiment,additional fixed function logic 2616 includes an additional geometrypipeline for use in position only shading. In position-only shading, atleast two geometry pipelines exist, whereas in a full geometry pipelinewithin geometry/fixed function pipeline 2616, 2636, and a cull pipeline,which is an additional geometry pipeline which may be included withinadditional fixed function logic 2616. In at least one embodiment, cullpipeline is a trimmed down version of a full geometry pipeline. In atleast one embodiment, a full pipeline and a cull pipeline can executedifferent instances of an application, each instance having a separatecontext. In at least one embodiment, position only shading can hide longcull runs of discarded triangles, enabling shading to be completedearlier in some instances. For example, in at least one embodiment, cullpipeline logic within additional fixed function logic 2616 can executeposition shaders in parallel with a main application and generallygenerates critical results faster than a full pipeline, as cull pipelinefetches and shades position attribute of vertices, without performingrasterization and rendering of pixels to a frame buffer. In at least oneembodiment, cull pipeline can use generated critical results to computevisibility information for all triangles without regard to whether thosetriangles are culled. In at least one embodiment, full pipeline (whichin this instance may be referred to as a replay pipeline) can consumevisibility information to skip culled triangles to shade only visibletriangles that are finally passed to a rasterization phase.

In at least one embodiment, additional fixed function logic 2616 canalso include machine-learning acceleration logic, such as fixed functionmatrix multiplication logic, for implementations including optimizationsfor machine learning training or inferencing.

In at least one embodiment, within each graphics sub-core 2601A-2601Fincludes a set of execution resources that may be used to performgraphics, media, and compute operations in response to requests bygraphics pipeline, media pipeline, or shader programs. In at least oneembodiment, graphics sub-cores 2601A-2601F include multiple EU arrays2602A-2602F, 2604A-2604F, thread dispatch and inter-thread communication(TD/IC) logic 2603A-2603F, a 3D (e.g., texture) sampler 2605A-2605F, amedia sampler 2606A-2606F, a shader processor 2607A-2607F, and sharedlocal memory (SLM) 2608A-2608F. EU arrays 2602A-2602F, 2604A-2604F eachinclude multiple execution units, which are general-purpose graphicsprocessing units capable of performing floating-point andinteger/fixed-point logic operations in service of a graphics, media, orcompute operation, including graphics, media, or compute shaderprograms. In at least one embodiment, TD/IC logic 2603A-2603F performslocal thread dispatch and thread control operations for execution unitswithin a sub-core and facilitate communication between threads executingon execution units of a sub-core. In at least one embodiment, 3D sampler2605A-2605F can read texture or other 3D graphics related data intomemory. In at least one embodiment, 3D sampler can read texture datadifferently based on a configured sample state and texture formatassociated with a given texture. In at least one embodiment, mediasampler 2606A-2606F can perform similar read operations based on a typeand format associated with media data. In at least one embodiment, eachgraphics sub-core 2601A-2601F can alternately include a unified 3D andmedia sampler. In at least one embodiment, threads executing onexecution units within each of sub-cores 2601A-2601F can make use ofshared local memory 2608A-2608F within each sub-core, to enable threadsexecuting within a thread group to execute using a common pool ofon-chip memory.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 26 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one graphics processor core 2600 is used to causeinformation received from a plurality of 5G new radio antennas to bedecoded in parallel by a plurality of processor pipelines. In at leastone embodiment, at least one graphics processor core 2600 is used tolayer demap, descramble, and de-rate-match 5G NR PUSCH data that hasbeen soft demapped in preparation for LDPC decoding, where at least onethread block is used to perform operations on code block data elements(e.g., LLRs) in parallel.

FIGS. 27A and 27B illustrate thread execution logic 2700 including anarray of processing elements of a graphics processor core according toat least one embodiment. FIG. 27A illustrates at least one embodiment,in which thread execution logic 2700 is used. FIG. 27B illustratesexemplary internal details of an execution unit, according to at leastone embodiment.

As illustrated in FIG. 27A, in at least one embodiment, thread executionlogic 2700 includes a shader processor 2702, a thread dispatcher 2704,instruction cache 2706, a scalable execution unit array including aplurality of execution units 2708A-2708N, a sampler 2710, a data cache2712, and a data port 2714. In at least one embodiment a scalableexecution unit array can dynamically scale by enabling or disabling oneor more execution units (e.g., any of execution unit 2708A, 2708B,2708C, 2708D, through 2708N-1 and 2708N) based on computationalrequirements of a workload, for example. In at least one embodiment,scalable execution units are interconnected via an interconnect fabricthat links to each of execution unit. In at least one embodiment, threadexecution logic 2700 includes one or more connections to memory, such assystem memory or cache memory, through one or more of instruction cache2706, data port 2714, sampler 2710, and execution units 2708A-2708N. Inat least one embodiment, each execution unit (e.g., 2708A) is astand-alone programmable general-purpose computational unit that iscapable of executing multiple simultaneous hardware threads whileprocessing multiple data elements in parallel for each thread. In atleast one embodiment, array of execution units 2708A-2708N is scalableto include any number individual execution units.

In at least one embodiment, execution units 2708A-2708N are primarilyused to execute shader programs. In at least one embodiment, shaderprocessor 2702 can process various shader programs and dispatchexecution threads associated with shader programs via a threaddispatcher 2704. In at least one embodiment, thread dispatcher 2704includes logic to arbitrate thread initiation requests from graphics andmedia pipelines and instantiate requested threads on one or moreexecution units in execution units 2708A-2708N. For example, in at leastone embodiment, a geometry pipeline can dispatch vertex, tessellation,or geometry shaders to thread execution logic for processing. In atleast one embodiment, thread dispatcher 2704 can also process runtimethread spawning requests from executing shader programs.

In at least one embodiment, execution units 2708A-2708N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. In at least one embodiment, execution units support vertexand geometry processing (e.g., vertex programs, geometry programs,vertex shaders), pixel processing (e.g., pixel shaders, fragmentshaders) and general-purpose processing (e.g., compute and mediashaders). In at least one embodiment, each of execution units2708A-2708N, which include one or more arithmetic logic units (ALUs), iscapable of multi-issue single instruction multiple data (SIMD) executionand multi-threaded operation enables an efficient execution environmentdespite higher latency memory accesses. In at least one embodiment, eachhardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state. Inat least one embodiment, execution is multi-issue per clock to pipelinescapable of integer, single and double precision floating pointoperations, SIMD branch capability, logical operations, transcendentaloperations, and other miscellaneous operations. In at least oneembodiment, while waiting for data from memory or one of sharedfunctions, dependency logic within execution units 2708A-2708N causes awaiting thread to sleep until requested data has been returned. In atleast one embodiment, while a waiting thread is sleeping, hardwareresources may be devoted to processing other threads. For example, in atleast one embodiment, during a delay associated with a vertex shaderoperation, an execution unit can perform operations for a pixel shader,fragment shader, or another type of shader program, including adifferent vertex shader.

In at least one embodiment, each execution unit in execution units2708A-2708N operates on arrays of data elements. In at least oneembodiment, a number of data elements is “execution size,” or number ofchannels for an instruction. In at least one embodiment, an executionchannel is a logical unit of execution for data element access, masking,and flow control within instructions. In at least one embodiment, anumber of channels may be independent of a number of physical ArithmeticLogic Units (ALUs) or Floating Point Units (FPUs) for a particulargraphics processor. In at least one embodiment, execution units2708A-2708N support integer and floating-point data types.

In at least one embodiment, an execution unit instruction set includesSIMD instructions. In at least one embodiment, various data elements canbe stored as a packed data type in a register and execution unit willprocess various elements based on data size of elements. For example, inat least one embodiment, when operating on a 256-bit wide vector, 256bits of a vector are stored in a register and an execution unit operateson a vector as four separate 64-bit packed data elements (Quad-Word (QW)size data elements), eight separate 32-bit packed data elements (DoubleWord (DW) size data elements), sixteen separate 16-bit packed dataelements (Word (W) size data elements), or thirty-two separate 8-bitdata elements (byte (B) size data elements). However, in at least oneembodiment, different vector widths and register sizes are possible.

In at least one embodiment, one or more execution units can be combinedinto a fused execution unit 2709A-2709N having thread control logic(2707A-2707N) that is common to fused EUs. In at least one embodiment,multiple EUs can be fused into an EU group. In at least one embodiment,each EU in fused EU group can be configured to execute a separate SIMDhardware thread. The number of EUs in a fused EU group can varyaccording to various embodiments. In at least one embodiment, variousSIMD widths can be performed per-EU, including but not limited to SIMD8,SIMD16, and SIMD32. In at least one embodiment, each fused graphicsexecution unit 2709A-2709N includes at least two execution units. Forexample, in at least one embodiment, fused execution unit 2709A includesa first EU 2708A, second EU 2708B, and thread control logic 2707A thatis common to first EU 2708A and second EU 2708B. In at least oneembodiment, thread control logic 2707A controls threads executed onfused graphics execution unit 2709A, allowing each EU within fusedexecution units 2709A-2709N to execute using a common instructionpointer register.

In at least one embodiment, one or more internal instruction caches(e.g., 2706) are included in thread execution logic 2700 to cache threadinstructions for execution units. In at least one embodiment, one ormore data caches (e.g., 2712) are included to cache thread data duringthread execution. In at least one embodiment, a sampler 2710 is includedto provide texture sampling for 3D operations and media sampling formedia operations. In at least one embodiment, sampler 2710 includesspecialized texture or media sampling functionality to process textureor media data during sampling process before providing sampled data toan execution unit.

During execution, in at least one embodiment, graphics and mediapipelines send thread initiation requests to thread execution logic 2700via thread spawning and dispatch logic. In at least one embodiment, oncea group of geometric objects has been processed and rasterized intopixel data, pixel processor logic (e.g., pixel shader logic, fragmentshader logic, etc.) within shader processor 2702 is invoked to furthercompute output information and cause results to be written to outputsurfaces (e.g., color buffers, depth buffers, stencil buffers, etc.). Inat least one embodiment, a pixel shader or fragment shader calculatesvalues of various vertex attributes that are to be interpolated across arasterized object. In at least one embodiment, pixel processor logicwithin shader processor 2702 then executes an application programminginterface (API)-supplied pixel or fragment shader program. In at leastone embodiment, to execute a shader program, shader processor 2702dispatches threads to an execution unit (e.g., 2708A) via threaddispatcher 2704. In at least one embodiment, shader processor 2702 usestexture sampling logic in sampler 2710 to access texture data in texturemaps stored in memory. In at least one embodiment, arithmetic operationson texture data and input geometry data compute pixel color data foreach geometric fragment, or discards one or more pixels from furtherprocessing.

In at least one embodiment, data port 2714 provides a memory accessmechanism for thread execution logic 2700 to output processed data tomemory for further processing on a graphics processor output pipeline.In at least one embodiment, data port 2714 includes or couples to one ormore cache memories (e.g., data cache 2712) to cache data for memoryaccess via a data port.

As illustrated in FIG. 27B, in at least one embodiment, a graphicsexecution unit 2708 can include an instruction fetch unit 2737, ageneral register file array (GRF) 2724, an architectural register filearray (ARF) 2726, a thread arbiter 2722, a send unit 2730, a branch unit2732, a set of SIMD floating point units (FPUs) 2734, and In at leastone embodiment a set of dedicated integer SIMD ALUs 2735. In at leastone embodiment, GRF 2724 and ARF 2726 includes a set of general registerfiles and architecture register files associated with each simultaneoushardware thread that may be active in graphics execution unit 2708. Inat least one embodiment, per thread architectural state is maintained inARF 2726, while data used during thread execution is stored in GRF 2724.In at least one embodiment, execution state of each thread, includinginstruction pointers for each thread, can be held in thread-specificregisters in ARF 2726.

In at least one embodiment, graphics execution unit 2708 has anarchitecture that is a combination of Simultaneous Multi-Threading (SMT)and fine-grained Interleaved Multi-Threading (IMT). In at least oneembodiment, architecture has a modular configuration that can befine-tuned at design time based on a target number of simultaneousthreads and number of registers per execution unit, where execution unitresources are divided across logic used to execute multiple simultaneousthreads.

In at least one embodiment, graphics execution unit 2708 can co-issuemultiple instructions, which may each be different instructions. In atleast one embodiment, thread arbiter 2722 of graphics execution unitthread 2708 can dispatch instructions to one of send unit 2730, branchunit 2742, or SIMD FPU(s) 2734 for execution. In at least oneembodiment, each execution thread can access 128 general-purposeregisters within GRF 2724, where each register can store 32 bytes,accessible as a SIMD 8-element vector of 32-bit data elements. In atleast one embodiment, each execution unit thread has access to 4 Kbyteswithin GRF 2724, although embodiments are not so limited, and greater orfewer register resources may be provided in other embodiments. In atleast one embodiment, up to seven threads can execute simultaneously,although a number of threads per execution unit can also vary accordingto embodiments. In at least one embodiment, in which seven threads mayaccess 4 Kbytes, GRF 2724 can store a total of 28 Kbytes. In at leastone embodiment, flexible addressing modes can permit registers to beaddressed together to build effectively wider registers or to representstrided rectangular block data structures.

In at least one embodiment, memory operations, sampler operations, andother longer-latency system communications are dispatched via “send”instructions that are executed by message passing send unit 2730. In atleast one embodiment, branch instructions are dispatched to a dedicatedbranch unit 2732 to facilitate SIMD divergence and eventual convergence.

In at least one embodiment graphics execution unit 2708 includes one ormore SIMD floating point units (FPU(s)) 2734 to perform floating-pointoperations. In at least one embodiment, FPU(s) 2734 also support integercomputation. In at least one embodiment FPU(s) 2734 can SIMD execute upto M number of 32-bit floating-point (or integer) operations, or SIMDexecute up to 2M 16-bit integer or 16-bit floating-point operations. Inat least one embodiment, at least one of FPU(s) provides extended mathcapability to support high-throughput transcendental math functions anddouble precision 64-bit floating-point. In at least one embodiment, aset of 8-bit integer SIMD ALUs 2735 are also present, and may bespecifically optimized to perform operations associated with machinelearning computations.

In at least one embodiment, arrays of multiple instances of graphicsexecution unit 2708 can be instantiated in a graphics sub-core grouping(e.g., a sub-slice). In at least one embodiment execution unit 2708 canexecute instructions across a plurality of execution channels. In atleast one embodiment, each thread executed on graphics execution unit2708 is executed on a different channel.

In at least one embodiment, at least one component shown or describedwith respect to FIGS. 27A and 27B is utilized to implement techniquesand/or functions described in connection with FIGS. 1-6. In at least oneembodiment, at least one thread execution logic 2700 is used to causeinformation received from a plurality of 5G new radio antennas to bedecoded in parallel by a plurality of processor pipelines. In at leastone embodiment, at least one thread execution logic 2700 is used tolayer demap, descramble, and de-rate-match 5G NR PUSCH data that hasbeen soft demapped in preparation for LDPC decoding, where at least onethread block is used to perform operations on code block data elements(e.g., LLRs) in parallel.

FIG. 28 illustrates a parallel processing unit (“PPU”) 2800, accordingto at least one embodiment. In at least one embodiment, PPU 2800 isconfigured with machine-readable code that, if executed by PPU 2800,causes PPU 2800 to perform some or all of processes and techniquesdescribed throughout this disclosure. In at least one embodiment, PPU2800 is a multi-threaded processor that is implemented on one or moreintegrated circuit devices and that utilizes multithreading as alatency-hiding technique designed to process computer-readableinstructions (also referred to as machine-readable instructions orsimply instructions) on multiple threads in parallel. In at least oneembodiment, a thread refers to a thread of execution and is aninstantiation of a set of instructions configured to be executed by PPU2800. In at least one embodiment, PPU 2800 is a graphics processing unit(“GPU”) configured to implement a graphics rendering pipeline forprocessing three-dimensional (“3D”) graphics data in order to generatetwo-dimensional (“2D”) image data for display on a display device suchas a liquid crystal display (“LCD”) device. In at least one embodiment,PPU 2800 is utilized to perform computations such as linear algebraoperations and machine-learning operations. FIG. 28 illustrates anexample parallel processor for illustrative purposes only and should beconstrued as a non-limiting example of processor architecturescontemplated within scope of this disclosure and that any suitableprocessor may be employed to supplement and/or substitute for same.

In at least one embodiment, one or more PPUs 2800 are configured toaccelerate High Performance Computing (“HPC”), data center, and machinelearning applications. In at least one embodiment, PPU 2800 isconfigured to accelerate deep learning systems and applicationsincluding following non-limiting examples: autonomous vehicle platforms,deep learning, high-accuracy speech, image, text recognition systems,intelligent video analytics, molecular simulations, drug discovery,disease diagnosis, weather forecasting, big data analytics, astronomy,molecular dynamics simulation, financial modeling, robotics, factoryautomation, real-time language translation, online search optimizations,and personalized user recommendations, and more.

In at least one embodiment, PPU 2800 includes, without limitation, anInput/Output (“I/O”) unit 2806, a front-end unit 2810, a scheduler unit2812, a work distribution unit 2814, a hub 2816, a crossbar (“Xbar”)2820, one or more general processing clusters (“GPCs”) 2818, and one ormore partition units (“memory partition units”) 2822. In at least oneembodiment, PPU 2800 is connected to a host processor or other PPUs 2800via one or more high-speed GPU interconnects (“GPU interconnects”) 2808.In at least one embodiment, PPU 2800 is connected to a host processor orother peripheral devices via an interconnect 2802. In at least oneembodiment, PPU 2800 is connected to a local memory comprising one ormore memory devices (“memory”) 2804. In at least one embodiment, memorydevices 2804 include, without limitation, one or more dynamic randomaccess memory (“DRAM”) devices. In at least one embodiment, one or moreDRAM devices are configured and/or configurable as high-bandwidth memory(“HBM”) subsystems, with multiple DRAM dies stacked within each device.

In at least one embodiment, high-speed GPU interconnect 2808 may referto a wire-based multi-lane communications link that is used by systemsto scale and include one or more PPUs 2800 combined with one or morecentral processing units (“CPUs”), supports cache coherence between PPUs2800 and CPUs, and CPU mastering. In at least one embodiment, dataand/or commands are transmitted by high-speed GPU interconnect 2808through hub 2816 to/from other units of PPU 2800 such as one or morecopy engines, video encoders, video decoders, power management units,and other components which may not be explicitly illustrated in FIG. 28.

In at least one embodiment, I/O unit 2806 is configured to transmit andreceive communications (e.g., commands, data) from a host processor (notillustrated in FIG. 28) over system bus 2802. In at least oneembodiment, I/O unit 2806 communicates with host processor directly viasystem bus 2802 or through one or more intermediate devices such as amemory bridge. In at least one embodiment, I/O unit 2806 may communicatewith one or more other processors, such as one or more of PPUs 2800 viasystem bus 2802. In at least one embodiment, I/O unit 2806 implements aPeripheral Component Interconnect Express (“PCIe”) interface forcommunications over a PCIe bus. In at least one embodiment, I/O unit2806 implements interfaces for communicating with external devices.

In at least one embodiment, I/O unit 2806 decodes packets received viasystem bus 2802. In at least one embodiment, at least some packetsrepresent commands configured to cause PPU 2800 to perform variousoperations. In at least one embodiment, I/O unit 2806 transmits decodedcommands to various other units of PPU 2800 as specified by commands. Inat least one embodiment, commands are transmitted to front-end unit 2810and/or transmitted to hub 2816 or other units of PPU 2800 such as one ormore copy engines, a video encoder, a video decoder, a power managementunit, etc. (not explicitly illustrated in FIG. 28). In at least oneembodiment, I/O unit 2806 is configured to route communications betweenand among various logical units of PPU 2800.

In at least one embodiment, a program executed by host processor encodesa command stream in a buffer that provides workloads to PPU 2800 forprocessing. In at least one embodiment, a workload comprisesinstructions and data to be processed by those instructions. In at leastone embodiment, buffer is a region in a memory that is accessible (e.g.,read/write) by both host processor and PPU 2800—a host interface unitmay be configured to access buffer in a system memory connected tosystem bus 2802 via memory requests transmitted over system bus 2802 byI/O unit 2806. In at least one embodiment, host processor writes commandstream to buffer and then transmits a pointer to start of command streamto PPU 2800 such that front-end unit 2810 receives pointers to one ormore command streams and manages one or more command streams, readingcommands from command streams and forwarding commands to various unitsof PPU 2800.

In at least one embodiment, front-end unit 2810 is coupled to schedulerunit 2812 that configures various GPCs 2818 to process tasks defined byone or more command streams. In at least one embodiment, scheduler unit2812 is configured to track state information related to various tasksmanaged by scheduler unit 2812 where state information may indicatewhich of GPCs 2818 a task is assigned to, whether task is active orinactive, a priority level associated with task, and so forth. In atleast one embodiment, scheduler unit 2812 manages execution of aplurality of tasks on one or more of GPCs 2818.

In at least one embodiment, scheduler unit 2812 is coupled to workdistribution unit 2814 that is configured to dispatch tasks forexecution on GPCs 2818. In at least one embodiment, work distributionunit 2814 tracks a number of scheduled tasks received from schedulerunit 2812 and work distribution unit 2814 manages a pending task pooland an active task pool for each of GPCs 2818. In at least oneembodiment, pending task pool comprises a number of slots (e.g., 32slots) that contain tasks assigned to be processed by a particular GPC2818; active task pool may comprise a number of slots (e.g., 4 slots)for tasks that are actively being processed by GPCs 2818 such that asone of GPCs 2818 completes execution of a task, that task is evictedfrom active task pool for GPC 2818 and one of other tasks from pendingtask pool is selected and scheduled for execution on GPC 2818. In atleast one embodiment, if an active task is idle on GPC 2818, such aswhile waiting for a data dependency to be resolved, then active task isevicted from GPC 2818 and returned to pending task pool while anothertask in pending task pool is selected and scheduled for execution on GPC2818.

In at least one embodiment, work distribution unit 2814 communicateswith one or more GPCs 2818 via XBar 2820. In at least one embodiment,XBar 2820 is an interconnect network that couples many of units of PPU2800 to other units of PPU 2800 and can be configured to couple workdistribution unit 2814 to a particular GPC 2818. In at least oneembodiment, one or more other units of PPU 2800 may also be connected toXBar 2820 via hub 2816.

In at least one embodiment, tasks are managed by scheduler unit 2812 anddispatched to one of GPCs 2818 by work distribution unit 2814. GPC 2818is configured to process task and generate results. In at least oneembodiment, results may be consumed by other tasks within GPC 2818,routed to a different GPC 2818 via XBar 2820, or stored in memory 2804.In at least one embodiment, results can be written to memory 2804 viapartition units 2822, which implement a memory interface for reading andwriting data to/from memory 2804. In at least one embodiment, resultscan be transmitted to another PPU 2804 or CPU via high-speed GPUinterconnect 2808. In at least one embodiment, PPU 2800 includes,without limitation, a number U of partition units 2822 that is equal tonumber of separate and distinct memory devices 2804 coupled to PPU 2800.In at least one embodiment, partition unit 2822 will be described inmore detail herein in conjunction with FIG. 30.

In at least one embodiment, a host processor executes a driver kernelthat implements an application programming interface (“API”) thatenables one or more applications executing on host processor to scheduleoperations for execution on PPU 2800. In at least one embodiment,multiple compute applications are simultaneously executed by PPU 2800and PPU 2800 provides isolation, quality of service (“QoS”), andindependent address spaces for multiple compute applications. In atleast one embodiment, an application generates instructions (e.g., inform of API calls) that cause driver kernel to generate one or moretasks for execution by PPU 2800 and driver kernel outputs tasks to oneor more streams being processed by PPU 2800. In at least one embodiment,each task comprises one or more groups of related threads, which may bereferred to as a warp. In at least one embodiment, a warp comprises aplurality of related threads (e.g., 32 threads) that can be executed inparallel. In at least one embodiment, cooperating threads can refer to aplurality of threads including instructions to perform task and thatexchange data through shared memory. In at least one embodiment, threadsand cooperating threads are described in more detail, in accordance withat least one embodiment, in conjunction with FIG. 30.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 28 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one PPU 2800 is used to cause information receivedfrom a plurality of 5G new radio antennas to be decoded in parallel by aplurality of processor pipelines. In at least one embodiment, at leastone PPU 2800 is used to layer demap, descramble, and de-rate-match 5G NRPUSCH data that has been soft demapped in preparation for LDPC decoding,where at least one thread block is used to perform operations on codeblock data elements (e.g., LLRs) in parallel.

FIG. 29 illustrates a general processing cluster (“GPC”) 2900, accordingto at least one embodiment. In at least one embodiment, GPC 2900 is GPC2818 of FIG. 28. In at least one embodiment, each GPC 2900 includes,without limitation, a number of hardware units for processing tasks andeach GPC 2900 includes, without limitation, a pipeline manager 2902, apre-raster operations unit (“PROP”) 2904, a raster engine 2908, a workdistribution crossbar (“WDX”) 2916, a memory management unit (“MMU”)2918, one or more Data Processing Clusters (“DPCs”) 2906, and anysuitable combination of parts.

In at least one embodiment, operation of GPC 2900 is controlled bypipeline manager 2902. In at least one embodiment, pipeline manager 2902manages configuration of one or more DPCs 2906 for processing tasksallocated to GPC 2900. In at least one embodiment, pipeline manager 2902configures at least one of one or more DPCs 2906 to implement at least aportion of a graphics rendering pipeline. In at least one embodiment,DPC 2906 is configured to execute a vertex shader program on aprogrammable streaming multi-processor (“SM”) 2914. In at least oneembodiment, pipeline manager 2902 is configured to route packetsreceived from a work distribution unit to appropriate logical unitswithin GPC 2900, in at least one embodiment, and some packets may berouted to fixed function hardware units in PROP 2904 and/or rasterengine 2908 while other packets may be routed to DPCs 2906 forprocessing by a primitive engine 2912 or SM 2914. In at least oneembodiment, pipeline manager 2902 configures at least one of DPCs 2906to implement a neural network model and/or a computing pipeline.

In at least one embodiment, PROP unit 2904 is configured, in at leastone embodiment, to route data generated by raster engine 2908 and DPCs2906 to a Raster Operations (“ROP”) unit in partition unit 2822,described in more detail above in conjunction with FIG. 28. In at leastone embodiment, PROP unit 2904 is configured to perform optimizationsfor color blending, organize pixel data, perform address translations,and more. In at least one embodiment, raster engine 2908 includes,without limitation, a number of fixed function hardware units configuredto perform various raster operations, in at least one embodiment, andraster engine 2908 includes, without limitation, a setup engine, acoarse raster engine, a culling engine, a clipping engine, a fine rasterengine, a tile coalescing engine, and any suitable combination thereof.In at least one embodiment, setup engine receives transformed verticesand generates plane equations associated with geometric primitivedefined by vertices; plane equations are transmitted to coarse rasterengine to generate coverage information (e.g., an x, y coverage mask fora tile) for primitive; output of coarse raster engine is transmitted toculling engine where fragments associated with primitive that fail az-test are culled, and transmitted to a clipping engine where fragmentslying outside a viewing frustum are clipped. In at least one embodiment,fragments that survive clipping and culling are passed to fine rasterengine to generate attributes for pixel fragments based on planeequations generated by setup engine. In at least one embodiment, outputof raster engine 2908 comprises fragments to be processed by anysuitable entity such as by a fragment shader implemented within DPC2906.

In at least one embodiment, each DPC 2906 included in GPC 2900 comprise,without limitation, an M-Pipe Controller (“MPC”) 2910; primitive engine2912; one or more SMs 2914; and any suitable combination thereof. In atleast one embodiment, MPC 2910 controls operation of DPC 2906, routingpackets received from pipeline manager 2902 to appropriate units in DPC2906. In at least one embodiment, packets associated with a vertex arerouted to primitive engine 2912, which is configured to fetch vertexattributes associated with vertex from memory; in contrast, packetsassociated with a shader program may be transmitted to SM 2914.

In at least one embodiment, SM 2914 comprises, without limitation, aprogrammable streaming processor that is configured to process tasksrepresented by a number of threads. In at least one embodiment, SM 2914is multi-threaded and configured to execute a plurality of threads(e.g., 32 threads) from a particular group of threads concurrently andimplements a Single-Instruction, Multiple-Data (“SIMD”) architecturewhere each thread in a group of threads (e.g., a warp) is configured toprocess a different set of data based on same set of instructions. In atleast one embodiment, all threads in group of threads execute sameinstructions. In at least one embodiment, SM 2914 implements aSingle-Instruction, Multiple Thread (“SIMT”) architecture wherein eachthread in a group of threads is configured to process a different set ofdata based on same set of instructions, but where individual threads ingroup of threads are allowed to diverge during execution. In at leastone embodiment, a program counter, call stack, and execution state ismaintained for each warp, enabling concurrency between warps and serialexecution within warps when threads within warp diverge. In anotherembodiment, a program counter, call stack, and execution state ismaintained for each individual thread, enabling equal concurrencybetween all threads, within and between warps. In at least oneembodiment, execution state is maintained for each individual thread andthreads executing same instructions may be converged and executed inparallel for better efficiency. At least one embodiment of SM 2914 aredescribed in more detail herein.

In at least one embodiment, MMU 2918 provides an interface between GPC2900 and memory partition unit (e.g., partition unit 2822 of FIG. 28)and MMU 2918 provides translation of virtual addresses into physicaladdresses, memory protection, and arbitration of memory requests. In atleast one embodiment, MMU 2918 provides one or more translationlookaside buffers (“TLBs”) for performing translation of virtualaddresses into physical addresses in memory.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 29 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one GPC 2900 is used to cause information receivedfrom a plurality of 5G new radio antennas to be decoded in parallel by aplurality of processor pipelines. In at least one embodiment, at leastone GPC 2900 is used to layer demap, descramble, and de-rate-match 5G NRPUSCH data that has been soft demapped in preparation for LDPC decoding,where at least one thread block is used to perform operations on codeblock data elements (e.g., LLRs) in parallel.

FIG. 30 illustrates a memory partition unit 3000 of a parallelprocessing unit (“PPU”), in accordance with at least one embodiment. Inat least one embodiment, memory partition unit 3000 includes, withoutlimitation, a Raster Operations (“ROP”) unit 3002; a level two (“L2”)cache 3004; a memory interface 3006; and any suitable combinationthereof. Memory interface 3006 is coupled to memory. Memory interface3006 may implement 32, 64, 128, 1024-bit data buses, or like, forhigh-speed data transfer. In at least one embodiment, PPU incorporates Umemory interfaces 3006, one memory interface 3006 per pair of partitionunits 3000, where each pair of partition units 3000 is connected to acorresponding memory device. For example, in at least one embodiment,PPU may be connected to up to Y memory devices, such as high bandwidthmemory stacks or graphics double-data-rate, version 5, synchronousdynamic random access memory (“GDDR5 SDRAM”).

In at least one embodiment, memory interface 3006 implements a highbandwidth memory second generation (“HBM2”) memory interface and Yequals half U. In at least one embodiment, HBM2 memory stacks arelocated on same physical package as PPU, providing substantial power andarea savings compared with conventional GDDR5 SDRAM systems. In at leastone embodiment, each HBM2 stack includes, without limitation, fourmemory dies and Y equals 4, with each HBM2 stack including two 128-bitchannels per die for a total of 8 channels and a data bus width of 1024bits. In at least one embodiment, memory supports Single-ErrorCorrecting Double-Error Detecting (“SECDED”) Error Correction Code(“ECC”) to protect data. ECC provides higher reliability for computeapplications that are sensitive to data corruption.

In at least one embodiment, PPU implements a multi-level memoryhierarchy. In at least one embodiment, memory partition unit 3000supports a unified memory to provide a single unified virtual addressspace for central processing unit (“CPU”) and PPU memory, enabling datasharing between virtual memory systems. In at least one embodimentfrequency of a30esses by a PPU to memory located on other processors istraced to ensure that memory pages are moved to physical memory of PPUthat is a30essing pages more frequently. In at least one embodiment,high-speed GPU interconnect 2808 supports address translation servicesallowing PPU to directly a30ess a CPU's page tables and providing fulla30ess to CPU memory by PPU.

In at least one embodiment, copy engines transfer data between multiplePPUs or between PPUs and CPUs. In at least one embodiment, copy enginescan generate page faults for addresses that are not mapped into pagetables and memory partition unit 3000 then services page faults, mappingaddresses into page table, after which copy engine performs transfer. Inat least one embodiment, memory is pinned (i.e., non-pageable) formultiple copy engine operations between multiple processors,substantially reducing available memory. In at least one embodiment,with hardware page faulting, addresses can be passed to copy engineswithout regard as to whether memory pages are resident, and copy processis transparent.

Data from memory 2804 of FIG. 28 or other system memory is fetched bymemory partition unit 3000 and stored in L2 cache 3004, which is locatedon-chip and is shared between various GPCs, in a30ordance with at leastone embodiment. Each memory partition unit 3000, in at least oneembodiment, includes, without limitation, at least a portion of L2 cacheassociated with a corresponding memory device. In at least oneembodiment, lower level caches are implemented in various units withinGPCs. In at least one embodiment, each of SMs 2914 may implement a levelone (“L1”) cache wherein L1 cache is private memory that is dedicated toa particular SM 2914 and data from L2 cache 3004 is fetched and storedin each of L1 caches for processing in functional units of SMs 2914. Inat least one embodiment, L2 cache 3004 is coupled to memory interface3006 and XBar 2820.

ROP unit 3002 performs graphics raster operations related to pixelcolor, such as color compression, pixel blending, and more, in at leastone embodiment. ROP unit 3002, in at least one embodiment, implementsdepth testing in conjunction with raster engine 2908, receiving a depthfor a sample location associated with a pixel fragment from cullingengine of raster engine 2908. In at least one embodiment, depth istested against a corresponding depth in a depth buffer for a samplelocation associated with fragment. In at least one embodiment, iffragment passes depth test for sample location, then ROP unit 3002updates depth buffer and transmits a result of depth test to rasterengine 2908. It will be appreciated that number of partition units 3000may be different than number of GPCs and, therefore, each ROP unit 3002can, in at least one embodiment, be coupled to each of GPCs. In at leastone embodiment, ROP unit 3002 tracks packets received from differentGPCs and determines which that a result generated by ROP unit 3002 isrouted to through XBar 2820.

FIG. 31 illustrates a streaming multi-processor (“SM”) 3100, accordingto at least one embodiment. In at least one embodiment, SM 3100 is SM ofFIG. 29. In at least one embodiment, SM 3100 includes, withoutlimitation, an instruction cache 3102; one or more scheduler units 3104;a register file 3108; one or more processing cores (“cores”) 3110; oneor more special function units (“SFUs”) 3112; one or more load/storeunits (“LSUs”) 3114; an interconnect network 3116; a shared memory/levelone (“L1”) cache 3118; and any suitable combination thereof. In at leastone embodiment, a work distribution unit dispatches tasks for executionon general processing clusters (“GPCs”) of parallel processing units(“PPUs”) and each task is allocated to a particular Data ProcessingCluster (“DPC”) within a GPC and, if task is associated with a shaderprogram, task is allocated to one of SMs 3100. In at least oneembodiment, scheduler unit 3104 receives tasks from work distributionunit and manages instruction scheduling for one or more thread blocksassigned to SM 3100. In at least one embodiment, scheduler unit 3104schedules thread blocks for execution as warps of parallel threads,wherein each thread block is allocated at least one warp. In at leastone embodiment, each warp executes threads. In at least one embodiment,scheduler unit 3104 manages a plurality of different thread blocks,allocating warps to different thread blocks and then dispatchinginstructions from plurality of different cooperative groups to variousfunctional units (e.g., processing cores 3110, SFUs 3112, and LSUs 3114)during each clock cycle.

In at least one embodiment, Cooperative Groups may refer to aprogramming model for organizing groups of communicating threads thatallows developers to express granularity at which threads arecommunicating, enabling expression of richer, more efficient paralleldecompositions. In at least one embodiment, cooperative launch APIssupport synchronization amongst thread blocks for execution of parallelalgorithms. In at least one embodiment, applications of conventionalprogramming models provide a single, simple construct for synchronizingcooperating threads: a barrier across all threads of a thread block(e.g., syncthreads( ) function). However, In at least one embodiment,programmers may define groups of threads at smaller than thread blockgranularities and synchronize within defined groups to enable greaterperformance, design flexibility, and software reuse in form ofcollective group-wide function interfaces. In at least one embodiment,Cooperative Groups enables programmers to define groups of threadsexplicitly at sub-block (i.e., as small as a single thread) andmulti-block granularities, and to perform collective operations such assynchronization on threads in a cooperative group. programming modelsupports clean composition across software boundaries, so that librariesand utility functions can synchronize safely within their local contextwithout having to make assumptions about convergence. In at least oneembodiment, Cooperative Groups primitives enable new patterns ofcooperative parallelism, including, without limitation,producer-consumer parallelism, opportunistic parallelism, and globalsynchronization across an entire grid of thread blocks.

In at least one embodiment, a dispatch unit 3106 is configured totransmit instructions to one or more of functional units and schedulerunit 3104 includes, without limitation, two dispatch units 3106 thatenable two different instructions from same warp to be dispatched duringeach clock cycle. In at least one embodiment, each scheduler unit 3104includes a single dispatch unit 3106 or additional dispatch units 3106.

In at least one embodiment, each SM 3100, in at least one embodiment,includes, without limitation, register file 3108 that provides a set ofregisters for functional units of SM 3100. In at least one embodiment,register file 3108 is divided between each of functional units such thateach functional unit is allocated a dedicated portion of register file3108. In at least one embodiment, register file 3108 is divided betweendifferent warps being executed by SM 3100 and register file 3108provides temporary storage for operands connected to data paths offunctional units. In at least one embodiment, each SM 3100 comprises,without limitation, a plurality of L processing cores 3110. In at leastone embodiment, SM 3100 includes, without limitation, a large number(e.g., 128 or more) of distinct processing cores 3110. In at least oneembodiment, each processing core 3110, in at least one embodiment,includes, without limitation, a fully-pipelined, single-precision,double-precision, and/or mixed precision processing unit that includes,without limitation, a floating point arithmetic logic unit and aninteger arithmetic logic unit. In at least one embodiment, floatingpoint arithmetic logic units implement IEEE 754-2008 standard forfloating point arithmetic. In at least one embodiment, processing cores3110 include, without limitation, 64 single-precision (32-bit) floatingpoint cores, 64 integer cores, 32 double-precision (64-bit) floatingpoint cores, and 8 tensor cores.

Tensor cores are configured to perform matrix operations in accordancewith at least one embodiment. In at least one embodiment, one or moretensor cores are included in processing cores 3110. In at least oneembodiment, tensor cores are configured to perform deep learning matrixarithmetic, such as convolution operations for neural network trainingand inferencing. In at least one embodiment, each tensor core operateson a 4×4 matrix and performs a matrix multiply and accumulate operationD=A×B+C, where A, B, C, and D are 4×4 matrices.

In at least one embodiment, matrix multiply inputs A and B are 16-bitfloating point matrices and accumulation matrices C and D are 16-bitfloating point or 32-bit floating point matrices. In at least oneembodiment, tensor cores operate on 16-bit floating point input datawith 32-bit floating point accumulation. In at least one embodiment,16-bit floating point multiply uses 64 operations and results in a fullprecision product that is then accumulated using 32-bit floating pointa3lition with other intermediate products for a 4×4×4 matrix multiply.Tensor cores are used to perform much larger two-dimensional or higherdimensional matrix operations, built up from these smaller elements, inat least one embodiment. In at least one embodiment, an API, such asCUDA 9 C++ API, exposes specialized matrix load, matrix multiply andaccumulate, and matrix store operations to efficiently use tensor coresfrom a CUDA-C++ program. In at least one embodiment, at CUDA level,warp-level interface assumes 16×16 size matrices spanning all 32 threadsof warp.

In at least one embodiment, each SM 3100 comprises, without limitation,M SFUs 3112 that perform special functions (e.g., attribute evaluation,reciprocal square root, and like). In at least one embodiment, SFUs 3112include, without limitation, a tree traversal unit configured totraverse a hierarchical tree data structure. In at least one embodiment,SFUs 3112 include, without limitation, a texture unit configured toperform texture map filtering operations. In at least one embodiment,texture units are configured to load texture maps (e.g., a 2D array oftexels) from memory and sample texture maps to produce sampled texturevalues for use in shader programs executed by SM 3100. In at least oneembodiment, texture maps are stored in shared memory/L1 cache 3118. Inat least one embodiment, texture units implement texture operations suchas filtering operations using mip-maps (e.g., texture maps of varyinglevels of detail), in accordance with at least one embodiment. In atleast one embodiment, each SM 3100 includes, without limitation, twotexture units.

Each SM 3100 comprises, without limitation, N LSUs 3114 that implementload and store operations between shared memory/L1 cache 3118 andregister file 3108, in at least one embodiment. Each SM 3100 includes,without limitation, interconnect network 3116 that connects each offunctional units to register file 3108 and LSU 3114 to register file3108 and shared memory/L1 cache 3118 in at least one embodiment. In atleast one embodiment, interconnect network 3116 is a crossbar that canbe configured to connect any of functional units to any of registers inregister file 3108 and connect LSUs 3114 to register file 3108 andmemory locations in shared memory/L1 cache 3118.

In at least one embodiment, shared memory/L1 cache 3118 is an array ofon-chip memory that allows for data storage and communication between SM3100 and primitive engine and between threads in SM 3100, in at leastone embodiment. In at least one embodiment, shared memory/L1 cache 3118comprises, without limitation, 128 KB of storage capacity and is in pathfrom SM 3100 to partition unit. In at least one embodiment, sharedmemory/L1 cache 3118, in at least one embodiment, is used to cache readsand writes. In at least one embodiment, one or more of shared memory/L1cache 3118, L2 cache, and memory are backing stores.

Combining data cache and shared memory functionality into a singlememory block provides improved performance for both types of memoryaccesses, in at least one embodiment. In at least one embodiment,capacity is used or is usable as a cache by programs that do not useshared memory, such as if shared memory is configured to use half ofcapacity, texture and load/store operations can use remaining capacity.Integration within shared memory/L1 cache 3118 enables shared memory/L1cache 3118 to function as a high-throughput conduit for streaming datawhile simultaneously providing high-bandwidth and low-latency access tofrequently reused data, in accordance with at least one embodiment. Inat least one embodiment, when configured for general purpose parallelcomputation, a simpler configuration can be used compared with graphicsprocessing. In at least one embodiment, fixed function graphicsprocessing units are bypassed, creating a much simpler programmingmodel. In general purpose parallel computation configuration, workdistribution unit assigns and distributes blocks of threads directly toDPCs, in at least one embodiment. In at least one embodiment, threads ina block execute same program, using a unique thread ID in calculation toensure each thread generates unique results, using SM 3100 to executeprogram and perform calculations, shared memory/L1 cache 3118 tocommunicate between threads, and LSU 3114 to read and write globalmemory through shared memory/L1 cache 3118 and memory partition unit. Inat least one embodiment, when configured for general purpose parallelcomputation, SM 3100 writes commands that scheduler unit 3104 can use tolaunch new work on DPCs.

In at least one embodiment, PPU is included in or coupled to a desktopcomputer, a laptop computer, a tablet computer, servers, supercomputers,a smart-phone (e.g., a wireless, hand-held device), personal digitalassistant (“PDA”), a digital camera, a vehicle, a head mounted display,a hand-held electronic device, and more. In at least one embodiment, PPUis embodied on a single semiconductor substrate. In at least oneembodiment, PPU is included in a system-on-a-chip (“SoC”) along with oneor more other devices such as additional PPUs, memory, a reducedinstruction set computer (“RISC”) CPU, a memory management unit (“MMU”),a digital-to-analog converter (“DAC”), and like.

In at least one embodiment, PPU may be included on a graphics card thatincludes one or more memory devices. graphics card may be configured tointerface with a PCIe slot on a motherboard of a desktop computer. In atleast one embodiment, PPU may be an integrated graphics processing unit(“iGPU”) included in chipset of motherboard.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 31 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one streaming multiprocessor 3100 is used to causeinformation received from a plurality of 5G new radio antennas to bedecoded in parallel by a plurality of processor pipelines. In at leastone embodiment, at least one streaming multiprocessor 3100 is used tolayer demap, descramble, and de-rate-match 5G NR PUSCH data that hasbeen soft demapped in preparation for LDPC decoding, where at least onethread block is used to perform operations on code block data elements(e.g., LLRs) in parallel.

In at least one embodiment, a single semiconductor platform may refer toa sole unitary semiconductor-based integrated circuit or chip. In atleast one embodiment, multi-chip modules may be used with increasedconnectivity which simulate on-chip operation, and make substantialimprovements over utilizing a conventional central processing unit(“CPU”) and bus implementation. In at least one embodiment, variousmodules may also be situated separately or in various combinations ofsemiconductor platforms per desires of user.

Networks

FIG. 32 illustrates a network 3200 for communicating data within a 5Gwireless communications network, in accordance with at least oneembodiment. In at least one embodiment, network 3200 comprises a basestation 3206 having a coverage area 3204, a plurality of mobile devices3208, and a backhaul network 3202. In at least one embodiment, as shown,base station 3206 establishes uplink and/or downlink connections withmobile devices 3208, which serve to carry data from mobile devices 3208to base station 3206 and vice-versa. In at least one embodiment, datacarried over uplink/downlink connections may include data communicatedbetween mobile devices 3208, as well as data communicated to/from aremote-end (not shown) by way of backhaul network 3202. In at least oneembodiment, term “base station” refers to any component (or collectionof components) configured to provide wireless access to a network, suchas an enhanced base station (eNB), a macro-cell, a femtocell, a Wi-Fiaccess point (AP), or other wirelessly enabled devices. In at least oneembodiment, base stations may provide wireless access in accordance withone or more wireless communication protocols, e.g., long term evolution(LTE), LTE advanced (LTE-A), High Speed Packet Access (HSPA), Wi-Fi802.11a/b/g/n/ac, etc. In at least one embodiment, term “mobile device”refers to any component (or collection of components) capable ofestablishing a wireless connection with a base station, such as a userequipment (UE), a mobile station (STA), and other wirelessly enableddevices. In some embodiments, network 3200 may comprise various otherwireless devices, such as relays, low power nodes, etc.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 32 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one base station 3206 is used to cause informationreceived from a plurality of 5G new radio antennas to be decoded inparallel by a plurality of processor pipelines. In at least oneembodiment, at least one base station 3206 is used to layer demap,descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel.

FIG. 33 illustrates a network architecture 3300 for a 5G wirelessnetwork, in accordance with at least one embodiment. In at least oneembodiment, as shown, network architecture 3300 includes a radio accessnetwork (RAN) 3304, an evolved packet core (EPC) 3302, which may bereferred to as a core network, and a home network 3316 of a UE 3308attempting to access RAN 3304. In at least one embodiment, RAN 3304 andEPC 3302 form a serving wireless network. In at least one embodiment,RAN 3304 includes a base station 3306, and EPC 3302 includes a mobilitymanagement entity (MME) 3312, a serving gateway (SGW) 3310, and a packetdata network (PDN) gateway (PGW) 3314. In at least one embodiment, homenetwork 3316 includes an application server 3318 and a home subscriberserver (HSS) 3320. In at least one embodiment, HSS 3320 may be part ofhome network 3316, EPC 3302, and/or variations thereof.

In at least one embodiment, MME 3312 is a termination point in a networkfor ciphering/integrity protection for NAS signaling and handlessecurity key management. In at least one embodiment, it should beappreciated that term “MME” is used in 4G LTE networks, and that 5G LTEnetworks may include a Security Anchor Node (SEAN) or a Security AccessFunction (SEAF) that performs similar functions. In at least oneembodiment, terms “MME,” “SEAN,” and “SEAF” may be used interchangeably.In at least one embodiment, MME 3312 also provides control planefunction for mobility between LTE and 2G/3G access networks, as well asan interface to home networks of roaming UEs. In at least oneembodiment, SGW 3310 routes and forwards user data packets, while alsoacting as a mobility anchor for an user plane during handovers. In atleast one embodiment, PGW 3314 provides connectivity from UEs toexternal packet data networks by being a point of exit and entry oftraffic for UEs. In at least one embodiment, HSS 3320 is a centraldatabase that contains user-related and subscription-relatedinformation. In at least one embodiment, application server 3318 is acentral database that contains user-related information regardingvarious applications that may utilize and communicate via networkarchitecture 3300.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 33 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one base station 3306 is used to cause informationreceived from a plurality of 5G new radio antennas to be decoded inparallel by a plurality of processor pipelines. In at least oneembodiment, at least one base station 3306 is used to layer demap,descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel.

FIG. 34 is a diagram illustrating some basic functionality of a mobiletelecommunications network/system operating in accordance with LTE and5G principles, in accordance with at least one embodiment. In at leastone embodiment, a mobile telecommunications system includesinfrastructure equipment comprising base stations 3414 which areconnected to a core network 3402, which operates in accordance with aconventional arrangement which will be understood by those acquaintedwith communications technology. In at least one embodiment,infrastructure equipment 3414 may also be referred to as a base station,network element, enhanced NodeB (eNodeB) or a coordinating entity forexample, and provides a wireless access interface to one or morecommunications devices within a coverage area or cell represented by abroken line 3404, which may be referred to as a radio access network. Inat least one embodiment, one or more mobile communications devices 3406may communicate data via transmission and reception of signalsrepresenting data using a wireless access interface. In at least oneembodiment, core network 3402 may also provide functionality includingauthentication, mobility management, charging and so on forcommunications devices served by a network entity.

In at least one embodiment, mobile communications devices of FIG. 34 mayalso be referred to as communications terminals, user equipment (UE),terminal devices and so forth, and are configured to communicate withone or more other communications devices served by a same or a differentcoverage area via a network entity. In at least one embodiment, thesecommunications may be performed by transmitting and receiving signalsrepresenting data using a wireless access interface over two waycommunications links.

In at least one embodiment, as shown in FIG. 34, one of eNodeBs 3414 ais shown in more detail to include a transmitter 3412 for transmittingsignals via a wireless access interface to one or more communicationsdevices or UEs 3406, and a receiver 3410 to receive signals from one ormore UEs within coverage area 3404. In at least one embodiment,controller 3408 controls transmitter 3412 and receiver 3410 to transmitand receive signals via a wireless access interface. In at least oneembodiment, controller 3408 may perform a function of controllingallocation of communications resource elements of a wireless accessinterface and may in some examples include a scheduler for schedulingtransmissions via a wireless access interface for both uplink anddownlink.

In at least one embodiment, an example UE 3406 a is shown in more detailto include a transmitter 3420 for transmitting signals on an uplink of awireless access interface to eNodeB 3414 and a receiver 3418 forreceiving signals transmitted by eNodeB 3414 on a downlink via awireless access interface. In at least one embodiment, transmitter 3420and receiver 3418 are controlled by a controller 3416.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 34 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one base station 3414 is used to cause informationreceived from a plurality of 5G new radio antennas to be decoded inparallel by a plurality of processor pipelines. In at least oneembodiment, at least one base station 3414 is used to layer demap,descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel.

FIG. 35 illustrates a radio access network 3500, which may be part of a5G network architecture, in accordance with at least one embodiment. Inat least one embodiment, radio access network 3500 covers a geographicregion divided into a number of cellular regions (cells) that can beuniquely identified by a user equipment (UE) based on an identificationbroadcasted over a geographical area from one access point or basestation. In at least one embodiment, macrocells 3540, 3528, and 3516,and a small cell 3530, may include one or more sectors. In at least oneembodiment, a sector is a sub-area of a cell and all sectors within onecell are served by a same base station. In at least one embodiment, asingle logical identification belonging to that sector can identify aradio link within a sector. In at least one embodiment, multiple sectorswithin a cell can be formed by groups of antennas with each antennaresponsible for communication with UEs in a portion of a cell.

In at least one embodiment, each cell is served by a base station (BS).In at least one embodiment, a base station is a network element in aradio access network responsible for radio transmission and reception inone or more cells to or from a UE. In at least one embodiment, a basestation may also be referred to as a base transceiver station (BTS), aradio base station, a radio transceiver, a transceiver function, a basicservice set (BSS), an extended service set (ESS), an access point (AP),a Node B (NB), an eNode B (eNB), a gNode B (gNB), or some other suitableterminology. In at least one embodiment, base stations may include abackhaul interface for communication with a backhaul portion of anetwork. In at least one embodiment, a base station has an integratedantenna or is connected to an antenna or remote radio head (RRH) byfeeder cables.

In at least one embodiment, a backhaul may provide a link between a basestation and a core network, and in some examples, a backhaul may provideinterconnection between respective base stations. In at least oneembodiment, a core network is a part of a wireless communication systemthat is generally independent of radio access technology used in a radioaccess network. In at least one embodiment, various types of backhaulinterfaces, such as a direct physical connection, a virtual network, orlike using any suitable transport network, may be employed. In at leastone embodiment, some base stations may be configured as integratedaccess and backhaul (IAB) nodes, where a wireless spectrum may be usedboth for access links (i.e., wireless links with UEs), and for backhaullinks, which is sometimes referred to as wireless self-backhauling. Inat least one embodiment, through wireless self-backhauling, a wirelessspectrum utilized for communication between a base station and UE may beleveraged for backhaul communication, enabling fast and easy deploymentof highly dense small cell networks, as opposed to requiring each newbase station deployment to be outfitted with its own hard-wired backhaulconnection.

In at least one embodiment, high-power base stations 3536 and 3520 areshown in cells 3540 and 3528, and a high-power base station 3510 isshown controlling a remote radio head (RRH) 3512 in cell 3516. In atleast one embodiment, cells 3540, 3528, and 3516 may be referred to aslarge size cells or macrocells. In at least one embodiment, a low-powerbase station 3534 is shown in small cell 3530 (e.g., a microcell,picocell, femtocell, home base station, home Node B, home eNode B, etc.)which may overlap with one or more macrocells, and may be referred to asa small cell or small size cell. In at least one embodiment, cell sizingcan be done according to system design as well as component constraints.In at least one embodiment, a relay node may be deployed to extend sizeor coverage area of a given cell. In at least one embodiment, radioaccess network 3500 may include any number of wireless base stations andcells. In at least one embodiment, base stations 3536, 3520, 3510, 3534provide wireless access points to a core network for any number ofmobile apparatuses.

In at least one embodiment, a quadcopter or drone 3542 may be configuredto function as a base station. In at least one embodiment, a cell maynot necessarily be stationary, and a geographic area of a cell may moveaccording to a location of a mobile base station such as quadcopter3542.

In at least one embodiment, radio access network 3500 supports wirelesscommunications for multiple mobile apparatuses. In at least oneembodiment, a mobile apparatus is commonly referred to as user equipment(UE), but may also be referred to as a mobile station (MS), a subscriberstation, a mobile unit, a subscriber unit, a wireless unit, a remoteunit, a mobile device, a wireless device, a wireless communicationsdevice, a remote device, a mobile subscriber station, an access terminal(AT), a mobile terminal, a wireless terminal, a remote terminal, ahandset, a terminal, a user agent, a mobile client, a client, or someother suitable terminology. In at least one embodiment, a UE may be anapparatus that provides a user with access to network services.

In at least one embodiment, a “mobile” apparatus need not necessarilyhave a capability to move, and may be stationary. In at least oneembodiment, mobile apparatus or mobile device broadly refers to adiverse array of devices and technologies. In at least one embodiment, amobile apparatus may be a mobile, a cellular (cell) phone, a smartphone, a session initiation protocol (SIP) phone, a laptop, a personalcomputer (PC), a notebook, a netbook, a smartbook, a tablet, a personaldigital assistant (PDA), a broad array of embedded systems, e.g.,corresponding to an “Internet of things” (IoT), an automotive or othertransportation vehicle, a remote sensor or actuator, a robot or roboticsdevice, a satellite radio, a global positioning system (GPS) device, anobject tracking device, a drone, a multi-copter, a quad-copter, a remotecontrol device, a consumer and/or wearable device, such as eyewear, awearable camera, a virtual reality device, a smart watch, a health orfitness tracker, a digital audio player (e.g., MP3 player), a camera, agame console, a digital home or smart home device such as a home audio,video, and/or multimedia device, an appliance, a vending machine,intelligent lighting, a home security system, a smart meter, a securitydevice, a solar panel or solar array, a municipal infrastructure devicecontrolling electric power (e.g., a smart grid), lighting, water, etc.,an industrial automation and enterprise device, a logistics controller,agricultural equipment, military defense equipment, vehicles, aircraft,ships, and weaponry, etc. In at least one embodiment, a mobile apparatusmay provide for connected medicine or telemedicine support, i.e., healthcare at a distance. In at least one embodiment, telehealth devices mayinclude telehealth monitoring devices and telehealth administrationdevices, whose communication may be given preferential treatment orprioritized access over other types of information, e.g., in terms ofprioritized access for transport of critical service data, and/orrelevant QoS for transport of critical service data.

In at least one embodiment, cells of radio access network 3500 mayinclude UEs that may be in communication with one or more sectors ofeach cell. In at least one embodiment, UEs 3514 and 3508 may be incommunication with base station 3510 by way of RRH 3512; UEs 3522 and3526 may be in communication with base station 3520; UE 3532 may be incommunication with low-power base station 3534; UEs 3538 and 3518 may bein communication with base station 3536; and UE 3544 may be incommunication with mobile base station 3542. In at least one embodiment,each base station 3510, 3520, 3534, 3536, and 3542 may be configured toprovide an access point to a core network (not shown) for all UEs inrespective cells and transmissions from a base station (e.g., basestation 3536) to one or more UEs (e.g., UEs 3538 and 3518) may bereferred to as downlink (DL) transmission, while transmissions from a UE(e.g., UE 3538) to a base station may be referred to as uplink (UL)transmissions. In at least one embodiment, downlink may refer to apoint-to-multipoint transmission, which may be referred to as broadcastchannel multiplexing. In at least one embodiment, uplink may refer to apoint-to-point transmission.

In at least one embodiment, quadcopter 3542, which may be referred to asa mobile network node, may be configured to function as a UE within cell3540 by communicating with base station 3536. In at least oneembodiment, multiple UEs (e.g., UEs 3522 and 3526) may communicate witheach other using peer to peer (P2P) or sidelink signals 3524, which maybypass a base station such as base station 3520.

In at least one embodiment, ability for a UE to communicate whilemoving, independent of its location, is referred to as mobility. In atleast one embodiment, a mobility management entity (MME) sets up,maintains, and releases various physical channels between a UE and aradio access network. In at least one embodiment, DL-based mobility orUL-based mobility may be utilized by a radio access network 3500 toenable mobility and handovers (i.e., transfer of a UE's connection fromone radio channel to another). In at least one embodiment, a UE, in anetwork configured for DL-based mobility, may monitor various parametersof a signal from its serving cell as well as various parameters ofneighboring cells, and, depending on a quality of these parameters, a UEmay maintain communication with one or more neighboring cells. In atleast one embodiment, if signal quality from a neighboring cell exceedsthat from a serving cell for a given amount of time, or if a UE movesfrom one cell to another, a UE may undertake a handoff or handover froma serving cell to a neighboring (target) cell. In at least oneembodiment, UE 3518 (illustrated as a vehicle, although any suitableform of UE may be used) may move from a geographic area corresponding toa cell, such as serving cell 3540, to a geographic area corresponding toa neighbor cell, such as neighbor cell 3516. In at least one embodiment,UE 3518 may transmit a reporting message to its serving base station3536 indicating its condition when signal strength or quality from aneighbor cell 3516 exceeds that of its serving cell 3540 for a givenamount of time. In at least one embodiment, UE 3518 may receive ahandover command, and may undergo a handover to cell 3516.

In at least one embodiment, UL reference signals from each UE may beutilized by a network configured for UL-based mobility to select aserving cell for each UE. In at least one embodiment, base stations3536, 3520, and 3510/3512 may broadcast unified synchronization signals(e.g., unified Primary Synchronization Signals (PSSs), unified SecondarySynchronization Signals (SSSs) and unified Physical Broadcast Channels(PBCH)). In at least one embodiment, UEs 3538, 3518, 3522, 3526, 3514,and 3508 may receive unified synchronization signals, derive a carrierfrequency and slot timing from synchronization signals, and in responseto deriving timing, transmit an uplink pilot or reference signal. In atleast one embodiment, two or more cells (e.g., base stations 3536 and3510/3512) within radio access network 3500 may concurrently receive anuplink pilot signal transmitted by a UE (e.g., UE 3518). In at least oneembodiment, cells may measure a strength of a pilot signal, and a radioaccess network (e.g., one or more of base stations 3536 and 3510/3512and/or a central node within a core network) may determine a servingcell for UE 3518. In at least one embodiment, a network may continue tomonitor an uplink pilot signal transmitted by UE 3518 as UE 3518 movesthrough radio access network 3500. In at least one embodiment, a network3500 may handover UE 3518 from a serving cell to a neighboring cell,with or without informing UE 3518, when a signal strength or quality ofa pilot signal measured by a neighboring cell exceeds that of a signalstrength or quality measured by a serving cell.

In at least one embodiment, synchronization signals transmitted by basestations 3536, 3520, and 3510/3512 may be unified, but may not identifya particular cell and rather may identify a zone of multiple cellsoperating on a same frequency and/or with a same timing. In at least oneembodiment, zones in 5G networks or other next generation communicationnetworks enable uplink-based mobility framework and improves efficiencyof both a UE and a network, since amounts of mobility messages that needto be exchanged between a UE and a network may be reduced.

In at least one embodiment, air interface in a radio access network 3500may utilize unlicensed spectrum, licensed spectrum, or shared spectrum.In at least one embodiment, unlicensed spectrum provides for shared useof a portion of a spectrum without need for a government-grantedlicense, however, while compliance with some technical rules isgenerally still required to access an unlicensed spectrum, generally,any operator or device may gain access. In at least one embodiment,licensed spectrum provides for exclusive use of a portion of a spectrum,generally by virtue of a mobile network operator purchasing a licensefrom a government regulatory body. In at least one embodiment, sharedspectrum may fall between licensed and unlicensed spectrum, whereintechnical rules or limitations may be required to access a spectrum, buta spectrum may still be shared by multiple operators and/or multipleRATs. In at least one embodiment, for example, a holder of a license fora portion of licensed spectrum may provide licensed shared access (LSA)to share that spectrum with other parties, e.g., with suitablelicensee-determined conditions to gain access.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 35 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one base station of radio access network 3500, suchas a gNB, is used to cause information received from a plurality of 5Gnew radio antennas to be decoded in parallel by a plurality of processorpipelines. In at least one embodiment, at least one base station ofradio access network 3500, such as a gNB, is used to layer demap,descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel.

FIG. 36 provides an example illustration of a 5G mobile communicationssystem in which a plurality of different types of devices is used, inaccordance with at least one embodiment. In at least one embodiment, asshown in FIG. 36, a first base station 3618 may be provided to a largecell or macro cell in which transmission of signals is over severalkilometers. In at least one embodiment, however, system may also supporttransmission via a very small cell such as transmitted by a secondinfrastructure equipment 3616 which transmits and receives signals overa distance of hundreds of meters thereby forming a so called “Pico”cell. In at least one embodiment, a third type of infrastructureequipment 3612 may transmit and receive signals over a distance of tensof meters and therefore can be used to form a so called “Femto” cell.

In at least one embodiment, also shown in FIG. 36, different types ofcommunications devices may be used to transmit and receive signals viadifferent types of infrastructure equipment 3612, 3616, 3618 andcommunication of data may be adapted in accordance with different typesof infrastructure equipment using different communications parameters.In at least one embodiment, conventionally, a mobile communicationsdevice may be configured to communicate data to and from a mobilecommunications network via available communication resources of network.In at least one embodiment, a wireless access system is configured toprovide highest data rates to devices such as smart phones 3606. In atleast one embodiment, “internet of things” may be provided in which lowpower machine type communications devices transmit and receive data atvery low power, low bandwidth and may have a low complexity. In at leastone embodiment, an example of such a machine type communication device3614 may communicate via a Pico cell 3616. In at least one embodiment, avery high data rate and a low mobility may be characteristic ofcommunications with, for example, a television 3604 which may becommunicating via a Pico cell. In at least one embodiment, a very highdata rate and low latency may be required by a virtual reality headset3608. In at least one embodiment, a relay device 3610 may be deployed toextend size or coverage area of a given cell or network.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 36 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one base station, such as base station 3618, isused to cause information received from a plurality of 5G new radioantennas to be decoded in parallel by a plurality of processorpipelines. In at least one embodiment, at least one base station, suchas base station 3618, is used to layer demap, descramble, andde-rate-match 5G NR PUSCH data that has been soft demapped inpreparation for LDPC decoding, where at least one thread block is usedto perform operations on code block data elements (e.g., LLRs) inparallel.

FIG. 37 illustrates an example high level system 3700, in which at leastone embodiment may be used. In at least one embodiment, high levelsystem 3700 includes applications 3702, system software+libraries 3704,framework software 3706 and a datacenter infrastructure+resourceorchestrator 3708. In at least one embodiment, high level system 3700may be implemented as a cloud service, physical service, virtualservice, network service, and/or variations thereof.

In at least one embodiment, as shown in FIG. 37, datacenterinfrastructure+resource orchestrator 3708 may include 5G radio resourceorchestrator 3710, GPU packet processing & I/O 3712, and node computingresources (“node C.R.s”) 3716(1)-3716(N), where “N” represents anywhole, positive integer. In at least one embodiment, node C.R.s3716(1)-3716(N) may include, but are not limited to, any number ofcentral processing units (“CPUs”) or other processors (includingaccelerators, field programmable gate arrays (FPGAs), graphicsprocessors (“GPUs”), etc.), memory devices (e.g., dynamic read-onlymemory), storage devices (e.g., solid state or disk drives), networkinput/output (“NW I/O”) devices, network switches, virtual machines(“VMs”), power modules, and cooling modules, etc. In at least oneembodiment, one or more node C.R.s from among node C.R.s 3716(1)-3716(N)may be a server having one or more of above-mentioned computingresources.

In at least one embodiment, 5G radio resource orchestrator 3710 mayconfigure or otherwise control one or more node C.R.s 3716(1)-3716(N)and/or other various components and resources a 5G network architecturemay comprise. In at least one embodiment, 5G radio resource orchestrator3710 may include a software design infrastructure (“SDI”) managemententity for high level system 3700. In at least one embodiment, 5G radioresource orchestrator 3710 may include hardware, software or somecombination thereof. In at least one embodiment, 5G radio resourceorchestrator 3710 may be utilized to configure or otherwise controlvarious medium access control sublayers, radio access networks, physicallayers or sublayers, and/or variations thereof, which may be part of a5G network architecture. In at least one embodiment, 5G radio resourceorchestrator 3710 may configure or allocate grouped compute, network,memory or storage resources to support one or more workloads which maybe executed as part of a 5G network architecture.

In at least one embodiment, GPU packet processing & I/O 3712 mayconfigure or otherwise process various inputs and outputs, as well aspackets such as data packets, which may be transmitted/received as partof a 5G network architecture, which may be implemented by high levelsystem 3700. In at least one embodiment, a packet may be data formattedto be provided by a network and may be typically divided into controlinformation and payload (i.e., user data). In at least one embodiment,types of packets may include Internet Protocol version 4 (IPv4) packets,Internet Protocol version 6 (IPv6) packets, and Ethernet II framepackets. In at least one embodiment, control data of a data packet maybe classified into data integrity fields and semantic fields. In atleast one embodiment, network connections that a data packet may bereceived upon include a local area network, a wide-area network, avirtual private network, Internet, an intranet, an extranet, a publicswitched telephone network, an infrared network, a wireless network, asatellite network and any combination thereof.

In at least one embodiment, framework software 3706 includes an AI ModelArchitecture+Training+Use Cases 3722. In at least one embodiment, AIModel Architecture+Training+Use Cases 3722 may include tools, services,software or other resources to train one or more machine learning modelsor predict or infer information using one or more machine learningmodels according to one or more embodiments. For example, in at leastone embodiment, a machine learning model may be trained by calculatingweight parameters according to a neural network architecture usingsoftware and computing resources described above with respect to highlevel system 3700. In at least one embodiment, trained machine learningmodels corresponding to one or more neural networks may be used to inferor predict information using resources described above with respect tohigh level system 3700 by using weight parameters calculated through oneor more training techniques. In at least one embodiment, frameworksoftware 3706 may include a framework to support systemsoftware+libraries 3704 and applications 3702.

In at least one embodiment, system software+libraries 3704 orapplications 3702 may respectively include web-based service software orapplications, such as those provided by Amazon Web Services, GoogleCloud and Microsoft Azure. In at least one embodiment, frameworksoftware 3706 may include, but is not limited to, a type of free andopen-source software web application framework such as Apache Spark™(hereinafter “Spark”). In at least one embodiment, systemsoftware+libraries 3704 may include software used by at least portionsof node C.R.s 3716(1)-3716(N). In at least one embodiment, one or moretypes of software may include, but are not limited to, Internet web pagesearch software, e-mail virus scan software, database software, andstreaming video content software.

In at least one embodiment, PHY 3718 is a set of system software andlibraries configured to provide an interface with a physical layer of awireless technology, which may be a physical layer such as a 5G NewRadio (NR) physical layer. In at least one embodiment, an NR physicallayer utilizes a flexible and scalable design and may comprise variouscomponents and technologies, such as modulation schemes, waveformstructures, frame structures, reference signals, multi-antennatransmission and channel coding.

In at least one embodiment, a NR physical layer supports quadraturephase shift keying (QPSK), 16 quadrature amplitude modulation (QAM), 64QAM and 256 QAM modulation formats. In at least one embodiment,different modulation schemes for different user entity (UE) categoriesmay also be included in a NR physical layer. In at least one embodiment,a NR physical layer may utilize cyclic prefix orthogonal frequencydivision multiplexing (CP-OFDM) with a scalable numerology (subcarrierspacing, cyclic prefix) in both uplink (UL) and downlink (DL) up to atleast 52.6 GHz. In at least one embodiment, a NR physical layer maysupport discrete Fourier transform spread orthogonal frequency divisionmultiplexing (DFT-SOFDM) in UL for coverage-limited scenarios, withsingle stream transmissions (that is, without spatial multiplexing).

In at least one embodiment, a NR frame supports time division duplex(TDD) and frequency division duplex (FDD) transmissions and operation inboth licensed and unlicensed spectrum, which enables very low latency,fast hybrid automatic repeat request (HARQ) acknowledgements, dynamicTDD, coexistence with LTE and transmissions of variable length (forexample, short duration for ultra-reliable low-latency communications(URLLC) and long duration for enhanced mobile broadband (eMBB)). In atleast one embodiment, NR frame structure follows three key designprinciples to enhance forward compatibility and reduce interactionsbetween different features.

In at least one embodiment, a first principle is that transmissions areself-contained, which can refer to a scheme in which data in a slot andin a beam are decodable on its own without dependency on other slots andbeams. In at least one embodiment, this implies that reference signalsrequired for demodulation of data are included in a given slot and agiven beam. In at least one embodiment, a second principle is thattransmissions are well confined in time and frequency, which results ina scheme in which new types of transmissions in parallel with legacytransmissions may be introduced. In at least one embodiment, a thirdprinciple is avoiding static and/or strict timing relations across slotsand across different transmission directions. In at least oneembodiment, usage of a third principle can entail utilizing asynchronoushybrid automatic repeat request (HARQ) instead of predefinedretransmission time.

In at least one embodiment, NR frame structure also allows for rapidHARQ acknowledgement, in which decoding is performed during reception ofDL data and HARQ acknowledgement is prepared by a UE during a guardperiod, when switching from DL reception to UL transmission. In at leastone embodiment, to obtain low latency, a slot (or a set of slots in caseof slot aggregation) is front-loaded with control signals and referencesignals at a beginning of a slot (or set of slots).

In at least one embodiment, NR has an ultra-lean design that minimizesalways-on transmissions to enhance network energy efficiency and ensureforward compatibility. In at least one embodiment, reference signals inNR are transmitted only when necessary. In at least one embodiment, fourmain reference signals are demodulation reference signal (DMRS),phase-tracking reference signal (PTRS), sounding reference signal (SRS)and channel-state information reference signal (CSI-RS).

In at least one embodiment, DMRS is used to estimate a radio channel fordemodulation. In at least one embodiment, DMRS is UE-specific, can bebeamformed, confined in a scheduled resource, and transmitted only whennecessary, both in DL and UL. In at least one embodiment, to supportmultiple-layer multiple-input, multiple-output (MIMO) transmission,multiple orthogonal DMRS ports can be scheduled, one for each layer. Inat least one embodiment, a basic DMRS pattern is front loaded, as a DMRSdesign takes into account an early decoding requirement to supportlow-latency applications. In at least one embodiment, for low-speedscenarios, DMRS uses low density in a time domain. In at least oneembodiment, however, for high-speed scenarios, a time density of DMRS isincreased to track fast changes in a radio channel.

In at least one embodiment, PTRS is introduced in NR to enablecompensation of oscillator phase noise. In at least one embodiment,typically, phase noise increases as a function of oscillator carrierfrequency. In at least one embodiment, PTRS can therefore be utilized athigh carrier frequencies (such as mmWave) to mitigate phase noise. In atleast one embodiment, PTRS is UE-specific, confined in a scheduledresource and can be beamformed. In at least one embodiment, PTRS isconfigurable depending on a quality of oscillators, carrier frequency,OFDM sub-carrier spacing, and modulation and coding schemes used fortransmission.

In at least one embodiment, SRS is transmitted in UL to perform channelstate information (CSI) measurements mainly for scheduling and linkadaptation. In at least one embodiment, for NR, SRS is also utilized forreciprocity-based precoder design for massive MIMO and UL beammanagement. In at least one embodiment, SRS has a modular and flexibledesign to support different procedures and UE capabilities. In at leastone embodiment, an approach for channel state information referencesignal (CSI-RS) is similar.

In at least one embodiment, NR employs different antenna solutions andtechniques depending on which part of a spectrum is used for itsoperation. In at least one embodiment, for lower frequencies, a low tomoderate number of active antennas (up to around 32 transmitter chains)is assumed and FDD operation is common. In at least one embodiment,acquisition of CSI requires transmission of CSI-RS in a DL and CSIreporting in an UL. In at least one embodiment, limited bandwidthsavailable in this frequency region require high spectral efficiencyenabled by multi-user MIMO (MU-MIMO) and higher order spatialmultiplexing, which is achieved via higher resolution CSI reportingcompared with LTE.

In at least one embodiment, for higher frequencies, a larger number ofantennas can be employed in a given aperture, which increases acapability for beamforming and multi user (MU)-MIMO. In at least oneembodiment, here, spectrum allocations are of TDD type andreciprocity-based operation is assumed. In at least one embodiment,high-resolution CSI in a form of explicit channel estimations isacquired by UL channel sounding. In at least one embodiment, suchhigh-resolution CSI enables sophisticated precoding algorithms to beemployed at a base station (BS). In at least one embodiment, for evenhigher frequencies (in mmWave range) an analog beamformingimplementation is typically required currently, which limitstransmission to a single beam direction per time unit and radio chain.In at least one embodiment, since an isotropic antenna element is verysmall in this frequency region owing to a short carrier wavelength, agreat number of antenna elements is required to maintain coverage. In atleast one embodiment, beamforming needs to be applied at bothtransmitter and receiver ends to combat increased path loss, even forcontrol channel transmission.

In at least one embodiment, to support these diverse use cases, NRfeatures a highly flexible but unified CSI framework, in which there isreduced coupling between CSI measurement, CSI reporting and an actual DLtransmission in NR compared with LTE. In at least one embodiment, NRalso supports more advanced schemes such as multi-point transmission andcoordination. In at least one embodiment, control and data transmissionsfollow a self-contained principle, where all information required todecode a transmission (such as accompanying DMRS) is contained within atransmission itself. In at least one embodiment, as a result, a networkcan seamlessly change a transmission point or beam as an UE moves in anetwork.

In at least one embodiment, MAC 3720 is a set of system software andlibraries configured to provide an interface with a medium accesscontrol (MAC) layer, which may be part of a 5G network architecture. Inat least one embodiment, a MAC layer controls hardware responsible forinteraction with a wired, optical or wireless transmission medium. In atleast one embodiment, MAC provides flow control and multiplexing for atransmission medium.

In at least one embodiment, a MAC sublayer provides an abstraction of aphysical layer such that complexities of a physical link control areinvisible to a logical link control (LLC) and upper layers of a networkstack. In at least one embodiment, any LLC sublayer (and higher layers)may be used with any MAC. In at least one embodiment, any MAC can beused with any physical layer, independent of transmission medium. In atleast one embodiment, a MAC sublayer, when sending data to anotherdevice on a network, encapsulates higher-level frames into framesappropriate for a transmission medium, adds a frame check sequence toidentify transmission errors, and then forwards data to a physical layeras soon as appropriate channel access method permits it. In at least oneembodiment, MAC is also responsible for compensating for collisions if ajam signal is detected, in which a MAC may initiate retransmission.

In at least one embodiment, applications 3702 may include one or moretypes of applications used by at least portions of node C.R.s3716(1)-3716(N) and/or framework software 3706. In at least oneembodiment, one or more types of applications may include, but are notlimited to, any number of a genomics application, a cognitive compute,and a machine learning application, including training or inferencingsoftware, machine learning framework software (e.g., PyTorch,TensorFlow, Caffe, etc.) or other machine learning applications used inconjunction with one or more embodiments.

In at least one embodiment, RAN APIs 3714 may be a set of subroutinedefinitions, communication protocols, and/or software tools that providea method of communication with components of a radio access network(RAN) which may be part of a 5G network architecture. In at least oneembodiment, a radio access network is part of a network communicationssystem and may implement a radio access technology. In at least oneembodiment, radio access network functionality is typically provided bya silicon chip residing in both a core network as well as userequipment. Further information regarding a radio access network can befound in description of FIG. 35.

In at least one embodiment, high level system 3700 may use CPUs,application-specific integrated circuits (ASICs), GPUs, FPGAs, or otherhardware to perform training, inferencing, and/or other variousprocesses using above-described resources. In at least one embodiment,moreover, one or more software and/or hardware resources described abovemay be configured as a service to allow users to train or performinginferencing of information, such as image recognition, speechrecognition, or other artificial intelligence services, as well as otherservices such as services that allow users to configure and implementvarious aspects of a 5G network architecture.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 37 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one of PHY 3718 and/or at least one node C.R. 3716is used to cause information received from a plurality of 5G new radioantennas to be decoded in parallel by a plurality of processorpipelines. In at least one embodiment, PHY 3718 runs on at least onenode C.R. 3716 and is used to layer demap, descramble, and de-rate-match5G NR PUSCH data that has been soft demapped in preparation for LDPCdecoding, where at least one thread block is used to perform operationson code block data elements (e.g., LLRs) in parallel.

FIG. 38 illustrates an architecture of a system 3800 of a network, inaccordance with at least one embodiment. In at least one embodiment,system 3800 is shown to include a user equipment (UE) 3802 and a UE3804. In at least one embodiment, UEs 3802 and 3804 are illustrated assmartphones (e.g., handheld touchscreen mobile computing devicesconnectable to one or more cellular networks) but may also comprise anymobile or non-mobile computing device, such as Personal Data Assistants(PDAs), pagers, laptop computers, desktop computers, wireless handsets,or any computing device including a wireless communications interface.

In at least one embodiment, any of UEs 3802 and 3804 can comprise anInternet of Things (IoT) UE, which can comprise a network access layerdesigned for low-power IoT ap-plications utilizing short-lived UEconnections. In at least one embodiment, an IoT UE can utilizetechnologies such as machine-to-machine (M2M) or machine-typecommunications (MTC) for exchanging data with an MTC server or devicevia a public land mobile network (PLMN), Proximity-Based Service (ProSe)or device-to-device (D2D) communication, sensor networks, or IoTnetworks. In at least one embodiment, a M2M or MTC exchange of data maybe a machine-initiated exchange of data. In at least one embodiment, anIoT network describes interconnecting IoT UEs, which may includeuniquely identifiable embedded computing devices (within Internetinfrastructure), with short-lived connections. In at least oneembodiment, an IoT UEs may execute background applications (e.g., keepalive messages, status updates, etc.) to facilitate connections of anIoT network.

In at least one embodiment, UEs 3802 and 3804 may be configured toconnect, e.g., communicatively couple, with a radio access network (RAN)3816. In at least one embodiment, RAN 3816 may be, for example, anEvolved Universal Mobile Telecommunications System (UMTS) TerrestrialRadio Access Network (E-UTRAN), a NextGen RAN (NG RAN), or some othertype of RAN. In at least one embodiment, UEs 3802 and 3804 utilizeconnections 3812 and 3814, respectively, each of which comprises aphysical communications interface or layer. In at least one embodiment,connections 3812 and 3814 are illustrated as an air interface to enablecommunicative coupling, and can be consistent with cellularcommunications protocols, such as a Global System for MobileCommunications (GSM) protocol, a code-division multiple access (CDMA)network protocol, a Push-to-Talk (PTT) protocol, a PTT over Cellular(POC) protocol, a Universal Mobile Telecommunications System (UMTS)protocol, a 3GPP Long Term Evolution (LTE) protocol, a fifth generation(5G) protocol, a New Radio (NR) protocol, and variations thereof.

In at least one embodiment, UEs 3802 and 3804 may further directlyexchange communication data via a ProSe interface 3806. In at least oneembodiment, ProSe interface 3806 may alternatively be referred to as asidelink interface comprising one or more logical channels, includingbut not limited to a Physical Sidelink Control Channel (PSCCH), aPhysical Sidelink Shared Channel (PSSCH), a Physical Sidelink DiscoveryChannel (PSDCH), and a Physical Sidelink Broadcast Channel (PSBCH).

In at least one embodiment, UE 3804 is shown to be configured to accessan access point (AP) 3810 via connection 3808. In at least oneembodiment, connection 3808 can comprise a local wireless connection,such as a connection consistent with any IEEE 802.11 protocol, whereinAP 3810 would comprise a wireless fidelity (WiFi®) router. In at leastone embodiment, AP 3810 is shown to be connected to an Internet withoutconnecting to a core network of a wireless system.

In at least one embodiment, RAN 3816 can include one or more accessnodes that enable connections 3812 and 3814. In at least one embodiment,these access nodes (ANs) can be referred to as base stations (BSs),NodeBs, evolved NodeBs (eNBs), next Generation NodeBs (gNB), RAN nodes,and so forth, and can comprise ground stations (e.g., terrestrial accesspoints) or satellite stations providing coverage within a geographicarea (e.g., a cell). In at least one embodiment, RAN 3816 may includeone or more RAN nodes for providing macrocells, e.g., macro RAN node3818, and one or more RAN nodes for providing femtocells or picocells(e.g., cells having smaller coverage areas, smaller user capacity, orhigher bandwidth compared to macrocells), e.g., low power (LP) RAN node3820.

In at least one embodiment, any of RAN nodes 3818 and 3820 can terminatean air interface protocol and can be a first point of contact for UEs3802 and 3804. In at least one embodiment, any of RAN nodes 3818 and3820 can fulfill various logical functions for RAN 3816 including, butnot limited to, radio network controller (RNC) functions such as radiobearer management, uplink and downlink dynamic radio resource managementand data packet scheduling, and mobility management.

In at least one embodiment, UEs 3802 and 3804 can be configured tocommunicate using Orthogonal Frequency-Division Multiplexing (OFDM)communication signals with each other or with any of RAN nodes 3818 and3820 over a multi-carrier communication channel in accordance variouscommunication techniques, such as, but not limited to, an OrthogonalFrequency Division Multiple Access (OFDMA) communication technique(e.g., for downlink communications) or a Single Carrier FrequencyDivision Multiple Ac-cess (SC-FDMA) communication technique (e.g., foruplink and ProSe or sidelink communications), and/or variations thereof.In at least one embodiment, OFDM signals can comprise a plurality oforthogonal sub-carriers.

In at least one embodiment, a downlink resource grid can be used fordownlink transmissions from any of RAN nodes 3818 and 3820 to UEs 3802and 3804, while uplink transmissions can utilize similar techniques. Inat least one embodiment, a grid can be a time frequency grid, called aresource grid or time-frequency resource grid, which is a physicalresource in a downlink in each slot. In at least one embodiment, such atime frequency plane representation is a common practice for OFDMsystems, which makes it intuitive for radio resource allocation. In atleast one embodiment, each column and each row of a resource gridcorresponds to one OFDM symbol and one OFDM subcarrier, respectively. Inat least one embodiment, a duration of a resource grid in a time domaincorresponds to one slot in a radio frame. In at least one embodiment, asmallest time-frequency unit in a resource grid is denoted as a resourceelement. In at least one embodiment, each resource grid comprises anumber of resource blocks, which describe a mapping of certain physicalchannels to resource elements. In at least one embodiment, each resourceblock comprises a collection of resource elements. In at least oneembodiment, in a frequency domain, this may represent a smallestquantity of resources that currently can be allocated. In at least oneembodiment, there are several different physical downlink channels thatare conveyed using such resource blocks.

In at least one embodiment, a physical downlink shared channel (PDSCH)may carry user data and higher-layer signaling to UEs 3802 and 3804. Inat least one embodiment, a physical downlink control channel (PDCCH) maycarry information about a transport format and resource allocationsrelated to PDSCH channel, among other things. In at least oneembodiment, it may also inform UEs 3802 and 3804 about a transportformat, resource allocation, and HARQ (Hybrid Automatic Repeat Request)information related to an uplink shared channel. In at least oneembodiment, typically, downlink scheduling (assigning control and sharedchannel resource blocks to UE 3802 within a cell) may be performed atany of RAN nodes 3818 and 3820 based on channel quality information fedback from any of UEs 3802 and 3804. In at least one embodiment, downlinkresource assignment information may be sent on a PDCCH used for (e.g.,assigned to) each of UEs 3802 and 3804.

In at least one embodiment, a PDCCH may use control channel elements(CCEs) to convey control information. In at least one embodiment, beforebeing mapped to resource elements, PDCCH complex valued symbols mayfirst be organized into quadruplets, which may then be permuted using asub-block interleaver for rate matching. In at least one embodiment,each PDCCH may be transmitted using one or more of these CCEs, whereeach CCE may correspond to nine sets of four physical resource elementsknown as resource element groups (REGs). In at least one embodiment,four Quadrature Phase Shift Keying (QPSK) symbols may be mapped to eachREG. In at least one embodiment, PDCCH can be transmitted using one ormore CCEs, depending on a size of a downlink control information (DCI)and a channel condition. In at least one embodiment, there can be fouror more different PDCCH formats defined in LTE with different numbers ofCCEs (e.g., aggregation level, L=1, 2, 4, or 8).

In at least one embodiment, an enhanced physical downlink controlchannel (EPDCCH) that uses PDSCH resources may be utilized for controlinformation transmission. In at least one embodiment, EPDCCH may betransmitted using one or more enhanced control channel elements (ECCEs).In at least one embodiment, each ECCE may correspond to nine sets offour physical resource elements known as an enhanced resource elementgroups (EREGs). In at least one embodiment, an ECCE may have othernumbers of EREGs in some situations.

In at least one embodiment, RAN 3816 is shown to be communicativelycoupled to a core network (CN) 3838 via an S1 interface 3822. In atleast one embodiment, CN 3838 may be an evolved packet core (EPC)network, a NextGen Packet Core (NPC) network, or some other type of CN.In at least one embodiment, S1 interface 3822 is split into two parts:S1-U interface 3826, which carries traffic data between RAN nodes 3818and 3820 and serving gateway (S-GW) 3830, and a S1-mobility managemententity (MME) interface 3824, which is a signaling interface between RANnodes 3818 and 3820 and MMEs 3828.

In at least one embodiment, CN 3838 comprises MMEs 3828, S-GW 3830,Packet Data Network (PDN) Gateway (P-GW) 3834, and a home subscriberserver (HSS) 3832. In at least one embodiment, MMEs 3828 may be similarin function to a control plane of legacy Serving General Packet RadioService (GPRS) Support Nodes (SGSN). In at least one embodiment, MMEs3828 may manage mobility aspects in access such as gateway selection andtracking area list management. In at least one embodiment, HSS 3832 maycomprise a database for network users, including subscription relatedinformation to support a network entities' handling of communicationsessions. In at least one embodiment, CN 3838 may comprise one orseveral HSSs 3832, depending on a number of mobile subscribers, on acapacity of an equipment, on an organization of a network, etc. In atleast one embodiment, HSS 3832 can provide support for routing/roaming,authentication, authorization, naming/addressing resolution, locationdependencies, etc.

In at least one embodiment, S-GW 3830 may terminate a S1 interface 3822towards RAN 3816, and routes data packets between RAN 3816 and CN 3838.In at least one embodiment, S-GW 3830 may be a local mobility anchorpoint for inter-RAN node handovers and also may provide an anchor forinter-3GPP mobility. In at least one embodiment, other responsibilitiesmay include lawful intercept, charging, and some policy enforcement.

In at least one embodiment, P-GW 3834 may terminate an SGi interfacetoward a PDN. In at least one embodiment, P-GW 3834 may route datapackets between an EPC network 3838 and external networks such as anetwork including application server 3840 (alternatively referred to asapplication function (AF)) via an Internet Protocol (IP) interface 3842.In at least one embodiment, application server 3840 may be an elementoffering applications that use IP bearer resources with a core network(e.g., UMTS Packet Services (PS) domain, LTE PS data services, etc.). Inat least one embodiment, P-GW 3834 is shown to be communicativelycoupled to an application server 3840 via an IP communications interface3842. In at least one embodiment, application server 3840 can also beconfigured to support one or more communication services (e.g.,Voice-over-Internet Protocol (VoIP) sessions, PTT sessions, groupcommunication sessions, social networking services, etc.) for UEs 3802and 3804 via CN 3838.

In at least one embodiment, P-GW 3834 may further be a node for policyenforcement and charging data collection. In at least one embodiment,policy and Charging Enforcement Function (PCRF) 3836 is a policy andcharging control element of CN 3838. In at least one embodiment, in anon-roaming scenario, there may be a single PCRF in a Home Public LandMobile Network (HPLMN) associated with a UE's Internet ProtocolConnectivity Access Network (IP-CAN) session. In at least oneembodiment, in a roaming scenario with local breakout of traffic, theremay be two PCRFs associated with a UE's IP-CAN session: a Home PCRF(H-PCRF) within a HPLMN and a Visited PCRF (V-PCRF) within a VisitedPublic Land Mobile Network (VPLMN). In at least one embodiment, PCRF3836 may be communicatively coupled to application server 3840 via P-GW3834. In at least one embodiment, application server 3840 may signalPCRF 3836 to indicate a new service flow and select an appropriateQuality of Service (QoS) and charging parameters. In at least oneembodiment, PCRF 3836 may provision this rule into a Policy and ChargingEnforcement Function (PCEF) (not shown) with an appropriate traffic flowtemplate (TFT) and QoS class of identifier (QCI), which commences a QoSand charging as specified by application server 3840.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 38 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one component of RAN 3816, such as RAN node 3818 or3820, is used to cause information received from a plurality of 5G newradio antennas to be decoded in parallel by a plurality of processorpipelines. In at least one embodiment, at least one component of RAN3816, such as RAN node 3818 or 3820, is used to layer demap, descramble,and de-rate-match 5G NR PUSCH data that has been soft demapped inpreparation for LDPC decoding, where at least one thread block is usedto perform operations on code block data elements (e.g., LLRs) inparallel.

FIG. 39 illustrates example components of a device 3900 in accordancewith at least one embodiment. In at least one embodiment, device 3900may include application circuitry 3904, baseband circuitry 3908, RadioFrequency (RF) circuitry 3910, front-end module (FEM) circuitry 3902,one or more antennas 3912, and power management circuitry (PMC) 3906coupled together at least as shown. In at least one embodiment,components of illustrated device 3900 may be included in a UE or a RANnode. In at least one embodiment, device 3900 may include less elements(e.g., a RAN node may not utilize application circuitry 3904, andinstead include a processor/controller to process IP data received froman EPC). In at least one embodiment, device 3900 may include additionalelements such as, for example, memory/storage, display, camera, sensor,or input/output (I/O) interface. In at least one embodiment, componentsdescribed below may be included in more than one device (e.g., saidcircuitries may be separately included in more than one device forCloud-RAN (C-RAN) implementations).

In at least one embodiment, application circuitry 3904 may include oneor more application processors. In at least one embodiment, applicationcircuitry 3904 may include circuitry such as, but not limited to, one ormore single-core or multi-core processors. In at least one embodiment,processor(s) may include any combination of general purpose processorsand dedicated processors (e.g., graphics processors, applicationprocessors, etc.). In at least one embodiment, processors may be coupledwith or may include memory/storage and may be configured to executeinstructions stored in memory/storage to enable various applications oroperating systems to run on device 3900. In at least one embodiment,processors of application circuitry 3904 may process IP data packetsreceived from an EPC.

In at least one embodiment, baseband circuitry 3908 may includecircuitry such as, but not limited to, one or more single-core ormulti-core processors. In at least one embodiment, baseband circuitry3908 may include one or more baseband processors or control logic toprocess baseband signals received from a receive signal path of RFcircuitry 3910 and to generate baseband signals for a transmit signalpath of RF circuitry 3910. In at least one embodiment, basebandprocessing circuitry 3908 may interface with application circuitry 3904for generation and processing of baseband signals and for controllingoperations of RF circuitry 3910. In at least one embodiment, basebandcircuitry 3908 may include a third generation (3G) baseband processor3908A, a fourth generation (4G) baseband processor 3908B, a fifthgeneration (5G) baseband processor 3908C, or other baseband processor(s)3908D for other existing generations, generations in development or tobe developed (e.g., second generation (2G), sixth generation (6G),etc.). In at least one embodiment, baseband circuitry 3908 (e.g., one ormore of base-band processors 3908A-D) may handle various radio controlfunctions that enable communi-cation with one or more radio networks viaRF circuitry 3910. In at least one embodiment, some or all of afunctionality of baseband processors 3908A-D may be included in modulesstored in memory 3908G and executed via a Central Processing Unit (CPU)3908E. In at least one embodiment, radio control functions may include,but are not limited to, signal modulation/demodulation,encoding/decoding, radio frequency shifting, etc. In at least oneembodiment, modulation/demodulation circuitry of baseband circuitry 3908may include Fast-Fourier Transform (FFT), precoding, or constellationmapping/demapping functionality. In at least one embodiment,encoding/decoding circuitry of baseband circuitry 3908 may includeconvolution, tailbiting convolution, turbo, Viterbi, or Low DensityParity Check (LDPC) encoder/decoder functionality.

In at least one embodiment, baseband circuitry 3908 may include one ormore audio digital signal processor(s) (DSP) 3908F. In at least oneembodiment, audio DSP(s) 3908F may be include elements forcompression/decompression and echo cancellation and may include othersuitable processing elements in other embodiments. In at least oneembodiment, components of baseband circuitry may be suitably combined ina single chip, a single chipset, or disposed on a same circuit board insome embodiments. In at least one embodiment, some or all of constituentcomponents of baseband circuitry 3908 and application circuitry 3904 maybe implemented together such as, for example, on a system on a chip(SOC).

In at least one embodiment, baseband circuitry 3908 may provide forcommunication compatible with one or more radio technologies. In atleast one embodiment, baseband circuitry 3908 may support communicationwith an evolved universal terrestrial radio access network (EUTRAN) orother wireless metropolitan area networks (WMAN), a wireless local areanetwork (WLAN), a wireless personal area network (WPAN). In at least oneembodiment, baseband circuitry 3908 is configured to support radiocommunications of more than one wireless protocol and may be referred toas multimode baseband circuitry.

In at least one embodiment, RF circuitry 3910 may enable communicationwith wireless networks using modulated electromagnetic radiation througha non-solid medium. In at least one embodiment, RF circuitry 3910 mayinclude switches, filters, amplifiers, etc. to facilitate communicationwith a wireless network. In at least one embodiment, RF circuitry 3910may include a receive signal path which may include circuitry todown-convert RF signals received from FEM circuitry 3902 and providebaseband signals to baseband circuitry 3908. In at least one embodiment,RF circuitry 3910 may also include a transmit signal path which mayinclude circuitry to up-convert baseband signals provided by basebandcircuitry 3908 and provide RF output signals to FEM circuitry 3902 fortransmission.

In at least one embodiment, receive signal path of RF circuitry 3910 mayinclude mixer circuitry 3910A, amplifier circuitry 3910B and filtercircuitry 3910C. In at least one embodiment, a transmit signal path ofRF circuitry 3910 may include filter circuitry 3910C and mixer circuitry3910A. In at least one embodiment, RF circuitry 3910 may also includesynthesizer circuitry 3910D for synthesizing a frequency for use bymixer circuitry 3910A of a receive signal path and a transmit signalpath. In at least one embodiment, mixer circuitry 3910A of a receivesignal path may be configured to down-convert RF signals received fromFEM circuitry 3902 based on a synthesized frequency provided bysynthesizer circuitry 3910D. In at least one embodiment, amplifiercircuitry 3910B may be configured to amplify down-converted signals andfilter circuitry 3910C may be a low-pass filter (LPF) or band-passfilter (BPF) configured to remove unwanted signals from down-convertedsignals to generate output baseband signals. In at least one embodiment,output baseband signals may be provided to baseband circuitry 3908 forfurther processing. In at least one embodiment, output baseband signalsmay be zero-frequency baseband signals, although this is not arequirement. In at least one embodiment, mixer circuitry 3910A of areceive signal path may comprise passive mixers.

In at least one embodiment, mixer circuitry 3910A of a transmit signalpath may be configured to up-convert input baseband signals based on asynthesized frequency provided by synthesizer circuitry 3910D togenerate RF output signals for FEM circuitry 3902. In at least oneembodiment, baseband signals may be provided by baseband circuitry 3908and may be filtered by filter circuitry 3910C.

In at least one embodiment, mixer circuitry 3910A of a receive signalpath and mixer circuitry 3910A of a transmit signal path may include twoor more mixers and may be arranged for quadrature down conversion and upconversion, respectively. In at least one embodiment, mixer circuitry3910A of a receive signal path and mixer circuitry 3910A of a transmitsignal path may include two or more mixers and may be arranged for imagerejection (e.g., Hartley image rejection). In at least one embodiment,mixer circuitry 3910A of a receive signal path and mixer circuitry 3910Amay be arranged for direct down conversion and direct up conversion,respectively. In at least one embodiment, mixer circuitry 3910A of areceive signal path and mixer circuitry 3910A of a transmit signal pathmay be configured for super-heterodyne operation.

In at least one embodiment, output baseband signals and input basebandsignals may be analog baseband signals. In at least one embodiment,output baseband signals and input baseband signals may be digitalbaseband signals. In at least one embodiment, RF circuitry 3910 mayinclude analog-to-digital converter (ADC) and digital-to-analogconverter (DAC) circuitry and baseband circuitry 3908 may include adigital baseband interface to communicate with RF circuitry 3910.

In at least one embodiment, a separate radio IC circuitry may beprovided for processing signals for each spectrum In at least oneembodiment, synthesizer circuitry 3910D may be a fractional-Nsynthesizer or a fractional N/N+1 synthesizer. In at least oneembodiment, synthesizer circuitry 3910D may be a delta-sigmasynthesizer, a frequency multiplier, or a synthesizer comprising aphase-locked loop with a frequency divider.

In at least one embodiment, synthesizer circuitry 3910D may beconfigured to synthesize an output frequency for use by mixer circuitry3910A of RF circuitry 3910 based on a frequency input and a dividercontrol input. In at least one embodiment, synthesizer circuitry 3910Dmay be a fractional N/N+1 synthesizer.

In at least one embodiment, frequency input may be provided by avoltage-controlled oscillator (VCO). In at least one embodiment, dividercontrol input may be provided by either baseband circuitry 3908 orapplications processor 3904 depending on a desired output frequency. Inat least one embodiment, a divider control input (e.g., N) may bedetermined from a look-up table based on a channel indicated byapplications processor 3904.

In at least one embodiment, synthesizer circuitry 3910D of RF circuitry3910 may include a divider, a delay-locked loop (DLL), a multiplexer anda phase accumulator. In at least one embodiment, divider may be a dualmodulus divider (DMD) and phase accumulator may be a digital phaseaccumulator (DPA). In at least one embodiment, DMD may be configured todivide an input signal by either N or N+1 (e.g., based on a carry out)to provide a fractional division ratio. In at least one embodiment, DLLmay include a set of cascaded, tunable, delay elements, a phasedetector, a charge pump and a D-type flip-flop. In at least oneembodiment, delay elements may be configured to break a VCO period upinto Nd equal packets of phase, where Nd is a number of delay elementsin a delay line. In at least one embodiment, in this way, DLL providesnegative feedback to help ensure that total delay through a delay lineis one VCO cycle.

In at least one embodiment, synthesizer circuitry 3910D may beconfigured to generate a carrier frequency as an output frequency, whilein other embodiments, output frequency may be a multiple of a carrierfrequency (e.g., twice a carrier frequency, four times a carrierfrequency) and used in conjunction with quadrature generator and dividercircuitry to generate multiple signals at a carrier frequency withmultiple different phases with respect to each other. In at least oneembodiment, output frequency may be a LO frequency (fLO). In at leastone embodiment, RF circuitry 3910 may include an IQ/polar converter.

In at least one embodiment, FEM circuitry 3902 may include a receivesignal path which may include circuitry configured to operate on RFsignals received from one or more antennas 3912, amplify receivedsignals and provide amplified versions of received signals to RFcircuitry 3910 for further processing. In at least one embodiment, FEMcircuitry 3902 may also include a transmit signal path which may includecircuitry configured to amplify signals for transmission provided by RFcircuitry 3910 for transmission by one or more of one or more antennas3912. In at least one embodiment, amplification through a transmit orreceive signal paths may be done solely in RF circuitry 3910, solely inFEM 3902, or in both RF circuitry 3910 and FEM 3902.

In at least one embodiment, FEM circuitry 3902 may include a TX/RXswitch to switch between transmit mode and receive mode operation. In atleast one embodiment, FEM circuitry may include a receive signal pathand a transmit signal path. In at least one embodiment, a receive signalpath of FEM circuitry may include an LNA to amplify received RF signalsand provide amplified received RF signals as an output (e.g., to RFcircuitry 3910). In at least one embodiment, a transmit signal path ofFEM circuitry 3902 may include a power amplifier (PA) to amplify inputRF signals (e.g., provided by RF circuitry 3910), and one or morefilters to generate RF signals for subsequent transmission (e.g., by oneor more of one or more antennas 3912).

In at least one embodiment, PMC 3906 may manage power provided tobaseband circuitry 3908. In at least one embodiment, PMC 3906 maycontrol power-source selection, voltage scaling, battery charging, orDC-to-DC conversion. In at least one embodiment, PMC 3906 may often beincluded when device 3900 is capable of being powered by a battery, forexample, when device is included in a UE. In at least one embodiment,PMC 3906 may increase power conversion efficiency while providingdesirable implementation size and heat dissipation characteristics.

In at least one embodiment, PMC 3906 may be additionally oralternatively coupled with, and perform similar power managementoperations for, other components such as, but not limited to,application circuitry 3904, RF circuitry 3910, or FEM 3902.

In at least one embodiment, PMC 3906 may control, or otherwise be partof, various power saving mechanisms of device 3900. In at least oneembodiment, if device 3900 is in an RRC Connected state, where it isstill connected to a RAN node as it expects to receive traffic shortly,then it may enter a state known as Discontinuous Reception Mode (DRX)after a period of inactivity. In at least one embodiment, during thisstate, device 3900 may power down for brief intervals of time and thussave power.

In at least one embodiment, if there is no data traffic activity for anextended period of time, then device 3900 may transition off to an RRCIdle state, where it disconnects from a network and does not performoperations such as channel quality feedback, handover, etc. In at leastone embodiment, device 3900 goes into a very low power state and itperforms paging where again it periodically wakes up to listen to anetwork and then powers down again. In at least one embodiment, device3900 may not receive data in this state, in order to receive data, itmust transition back to RRC Connected state.

In at least one embodiment, an additional power saving mode may allow adevice to be unavailable to a network for periods longer than a paginginterval (ranging from seconds to a few hours). In at least oneembodiment, during this time, a device is totally unreachable to anetwork and may power down completely. In at least one embodiment, anydata sent during this time incurs a large delay and it is assumed delayis acceptable.

In at least one embodiment, processors of application circuitry 3904 andprocessors of baseband circuitry 3908 may be used to execute elements ofone or more instances of a protocol stack. In at least one embodiment,processors of baseband circuitry 3908, alone or in combination, may beused execute Layer 3, Layer 2, or Layer 1 functionality, whileprocessors of application circuitry 3908 may utilize data (e.g., packetdata) received from these layers and further execute Layer 4functionality (e.g., transmission communication protocol (TCP) and userdatagram protocol (UDP) layers). In at least one embodiment, layer 3 maycomprise a radio resource control (RRC) layer. In at least oneembodiment, Layer 2 may comprise a medium access control (MAC) layer, aradio link control (RLC) layer, and a packet data convergence protocol(PDCP) layer. In at least one embodiment, Layer 1 may comprise aphysical (PHY) layer of a UE/RAN node.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 39 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one component of device 3900, such as 5G basebandcircuitry 3908C, is used to cause information received from a pluralityof 5G new radio antennas to be decoded in parallel by a plurality ofprocessor pipelines. In at least one embodiment, at least one componentof device 3900, such as 5G baseband circuitry 3908C, is used to layerdemap, descramble, and de-rate-match 5G NR PUSCH data that has been softdemapped in preparation for LDPC decoding, where at least one threadblock is used to perform operations on code block data elements (e.g.,LLRs) in parallel.

FIG. 40 illustrates example interfaces of baseband circuitry, inaccordance with at least one embodiment. In at least one embodiment, asdiscussed above, baseband circuitry 3908 of FIG. 39 may compriseprocessors 3908A-3908E and a memory 3908G utilized by said processors.In at least one embodiment, each of processors 3908A-3908E may include amemory interface, 4002A-4002E, respectively, to send/receive datato/from memory 3908G.

In at least one embodiment, baseband circuitry 3908 may further includeone or more interfaces to communicatively couple to othercircuitries/devices, such as a memory interface 4004 (e.g., an interfaceto send/receive data to/from memory external to baseband circuitry3908), an application circuitry interface 4006 (e.g., an interface tosend/receive data to/from application circuitry 3904 of FIG. 39), an RFcircuitry interface 4008 (e.g., an interface to send/receive datato/from RF circuitry 3910 of FIG. 39), a wireless hardware connectivityinterface 4010 (e.g., an interface to send/receive data to/from NearField Communication (NFC) components, Bluetooth® components (e.g.,Bluetooth® Low Energy), Wi-Fi® components, and other communicationcomponents), and a power management interface 4012 (e.g., an interfaceto send/receive power or control signals to/from PMC 3906.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 40 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one component of baseband circuitry 4008, is usedto cause information received from a plurality of 5G new radio antennasto be decoded in parallel by a plurality of processor pipelines. In atleast one embodiment, at least one component of baseband circuitry 4008,is used to layer demap, descramble, and de-rate-match 5G NR PUSCH datathat has been soft demapped in preparation for LDPC decoding, where atleast one thread block is used to perform operations on code block dataelements (e.g., LLRs) in parallel.

FIG. 41 illustrates an example of an uplink channel, in accordance withat least one embodiment. In at least one embodiment, FIG. 41 illustratestransmitting and receiving data within a physical uplink shared channel(PUSCH) in 5G NR, which may be part of a physical layer of a mobiledevice network.

In at least one embodiment, Physical Uplink Shared Channel (PUSCH) in 5GNR is designated to carry multiplexed control information and userapplication data. In at least one embodiment, 5G NR provides much moreflexibility and reliability comparing to its predecessor, which in someexamples may be referred to as 4G LTE, including more elastic pilotarrangements and support for both cyclic prefix (CP)-OFDM and DiscreteFourier Transform spread (DFT-s)-OFDM waveforms. In at least oneembodiment, standard introduced filtered OFDM (f-OFDM) technique isutilized to add additional filtering to reduce Out-of-Band emission andimprove performance at higher modulation orders. In at least oneembodiment, modifications in Forward Error Correction (FEC) were imposedto replace Turbo Codes used in 4G LTE by Quasi-Cyclic Low Density ParityCheck (QC-LDPC) codes, which were proven to achieve better transmissionrates and provide opportunities for more efficient hardwareimplementations.

In at least one embodiment, transmission of 5G NR downlink and uplinkdata is organized into frames of 10 ms duration, each divided into 10subframes of 1 ms each. In at least one embodiment, subframes arecomposed of a variable number of slots, depending on a selectedsubcarrier spacing which is parameterized in 5G NR. In at least oneembodiment, a slot is built from 14 OFDMA symbols, each prepended with acyclic prefix. In at least one embodiment, a subcarrier that is locatedwithin a passband and is designated for transmission is called aResource Element (RE). In at least one embodiment, a group of 12neighboring RE in a same symbol form a Physical Resource Block (PRB).

In at least one embodiment, 5G NR standard defined two types ofreference signals associated with transmission within a PUSCH channel.In at least one embodiment, Demodulation Reference Signal (DMRS) is auser specific reference signal with high frequency density. In at leastone embodiment, DMRS is transmitted within dedicated orthogonalfrequency-division multiple access (OFDMA) symbols only and designatedfor frequency-selective channel estimation. In at least one embodiment,a number of DMRS symbols within a slot may vary between 1 and 4depending on configuration, where a denser DMRS symbol spacing in timeis designated for fast time-varying channels to obtain more accurateestimates within a coherence time of a channel. In at least oneembodiment, in a frequency domain, DMRS PRB are mapped within a wholetransmission allocation. In at least one embodiment, spacing between aDMRS resource element (RE) assigned for a same Antenna Port (AP) may bechosen between 2 and 3. In at least one embodiment, in a case of 2-2multiple-input, multiple-output (MIMO), a standard allows for orthogonalassignment of RE between AP. In at least one embodiment, a receiver mayperform partial single input, multiple output (SIMO) channel estimationbased on a DMRS RE prior to MIMO equalization, neglecting spatialcorrelation.

In at least one embodiment, a second type of reference signal is a PhaseTracking Reference Signal (PTRS). In at least one embodiment, PTRSsubcarriers are arranged in a comb structure having high density in atime domain. In at least one embodiment, it is used mainly in mmWavefrequency bands to track and correct phase noise, which is aconsiderable source of performance losses. In at least one embodiment,usage of PTRS is optional, as it may lower a total spectral efficiencyof a transmission when effects of phase noise are negligible.

In at least one embodiment, for transmission of data, a transport blockmay be generated from a MAC layer and given to a physical layer. In atleast one embodiment, a transport block may be data that is intended tobe transmitted. In at least one embodiment, a transmission in a physicallayer starts with grouped resource data, which may be referred to astransport blocks. In at least one embodiment, a transport block isreceived by a cyclic redundancy check (CRC) 4102. In at least oneembodiment, a cyclic redundancy check is appended to each transportblock for error detection. In at least one embodiment, a cyclicredundancy check is used for error detection in transport blocks. In atleast one embodiment, an entire transport block is used to calculate CRCparity bits and these parity bits are then attached to an end of atransport block. In at least one embodiment, minimum and maximum codeblock sizes are specified so blocks sizes are compatible with furtherprocesses. In at least one embodiment, an input block is segmented whenan input block is greater than a maximum code block size.

In at least one embodiment, a transport block is received and encoded bya low-density parity-check (LDPC) encode 4104. In at least oneembodiment, NR employs low-density parity-check (LDPC) codes for a datachannel and polar codes for a control channel. In at least oneembodiment, LDPC codes are defined by their parity-check matrices, witheach column representing a coded bit, and each row representing aparity-check equation. In at least one embodiment, LDPC codes aredecoded by exchanging messages between variables and parity checks in aniterative manner. In at least one embodiment, LDPC codes proposed for NRuse a quasi-cyclic structure, where a parity-check matrix is defined bya smaller base matrix. In at least one embodiment, each entry of basematrix represents either a Z×Z zero matrix or a shifted Z×Z identitymatrix.

In at least one embodiment, an encoded transport block is received byrate match 4106. In at least one embodiment, an encoded block is used tocreate an output bit stream with a desired code rate. In at least oneembodiment, rate match 4106 is utilized to create an output bit streamto be transmitted with a desired code rate. In at least one embodiment,bits are selected and pruned from a buffer to create an output bitstream with a desired code rate. In at least one embodiment, a HybridAutomatic Repeat Request (HARQ) error correction scheme is incorporated.

In at least one embodiment, output bits are scrambled, which may aid inprivacy, in scramble 4108. In at least one embodiment, codewords arebit-wise multiplied with an orthogonal sequence and a UE-specificscrambling sequence. In at least one embodiment, output of scramble 4108may be input into modulation/mapping/precoding and other processes 4110.In at least one embodiment, various modulation, mapping, and precodingprocesses are performed.

In at least one embodiment, bits output from scramble 4108 are modulatedwith a modulation scheme, resulting in blocks of modulation symbols. Inat least one embodiment, scrambled codewords undergo modulation usingone of modulation schemes QPSK, 16 QAM, 64 QAM, resulting in a block ofmodulation symbols. In at least one embodiment, a channel interleaverprocess may be utilized that implements a first time mapping ofmodulation symbols onto a transmit waveform while ensuring that HARQinformation is present on both slots. In at least one embodiment,modulation symbols are mapped to various layers based on transmitantennas. In at least one embodiment, symbols may be precoded, in whichthey are divided into sets, and an Inverse Fast Fourier Transform may beperformed. In at least one embodiment, transport data and controlmultiplexing may be performed such that HARQ acknowledge (ACK)information is present in both slots and is mapped to resources arounddemodulation reference signals. In at least one embodiment, variousprecoding processes are performed.

In at least one embodiment, symbols are mapped to allocated physicalresource elements in resource element mapping 4112. In at least oneembodiment, allocation sizes may be limited to values whose primefactors are 2, 3 and 5. In at least one embodiment, symbols are mappedin increasing order beginning with subcarriers. In at least oneembodiment, subcarrier mapped modulation symbols data are orthogonalfrequency-division multiple access (OFDMA) modulated through IFFToperation in OFDMA modulation 4114. In at least one embodiment, timedomain representations of each symbol are concatenated and filteredusing transmit FIR filter to attenuate unwanted Out of Band emission toadjacent frequency bands caused by phase discontinuities and utilizationof different numerologies. In at least one embodiment, an output ofOFDMA modulation 4114 may be transmitted to be received and processed byanother system.

In at least one embodiment, a transmission may be received by OFDMAdemodulation 4116. In at least one embodiment, a transmission mayoriginate from user mobile devices over a cellular network, althoughother contexts may be present. In at least one embodiment, atransmission may be demodulated through IFFT processing. In at least oneembodiment, once OFDMA demodulation through IFFT processing has beenaccomplished, an estimation and correction of residual Sample TimeOffset (STO) and Carrier Frequency Offset (CFO) may be performed. In atleast one embodiment, both CFO and STO corrections have to be performedin frequency domain, because a received signal can be a superposition oftransmissions coming from multiple UEs multiplexed in frequency, eachsuffering from a specific residual synchronization error. In at leastone embodiment, residual CFO is estimated as a phase rotation betweenpilot subcarriers belonging to different OFDM symbols and corrected by acircular convolution operation in frequency domain.

In at least one embodiment, output of OFDMA demodulation 4116 may bereceived by resource element demapping 4118. In at least one embodiment,resource element demapping 4118 may determine symbols and demap symbolsfrom allocated physical resource elements. In at least one embodiment, achannel estimation and equalization is performed in channel estimation4120 in order to compensate for effects of multipath propagation. In atleast one embodiment, channel estimation 4120 may be utilized tominimize effects of noise originating from various transmission layersand antennae. In at least one embodiment, channel estimation 4120 maygenerate equalized symbols from an output of resource element demapping4118. In at least one embodiment, demodulation/demapping 4122 mayreceive equalized symbols from channel estimation 4120. In at least oneembodiment, equalized symbols are demapped and permuted through a layerdemapping operation. In at least one embodiment, a Maximum A PosterioriProbability (MAP) demodulation approach may be utilized to producevalues representing beliefs regarding a received bit being 0 or 1,expressed in a form of Log-Likelihood Ratio (LLR).

In at least one embodiment, soft-demodulated bits are processed usingvarious operations, including descrambling, deinterleaving and rateunmatching with LLR soft-combining using a circular buffer prior to LDPCdecoding. In at least one embodiment, descramble 4124 may involveprocesses that reverse one or more processes of scramble 4108. In atleast one embodiment, rate unmatch 4126 may involve processes thatreverse one or more processes of rate match 4106. In at least oneembodiment, descramble 4124 may receive output fromdemodulation/demapping 4122, and descramble received bits. In at leastone embodiment, rate unmatch 4126 may receive descrambled bits, andutilize LLR soft-combining utilizing a circular buffer prior to LDPCdecode 4128.

In at least one embodiment, decoding of LDPC codes in practicalapplications is done based on iterative belief propagation algorithms.In at least one embodiment, an LDPC code can be represented in a form ofa bipartite graph with parity check matrix H of size M×N being abiadjacency matrix defining connections between graph nodes. In at leastone embodiment, M rows of matrix H corresponds to parity check nodes,whereas N columns corresponds to variable nodes, i.e. received codewordbits. In at least one embodiment, a principle of belief propagationalgorithms is based on iterative message exchange, in which A Posterioriprobabilities between a variable and check nodes are updated, until avalid codeword is obtained. In at least one embodiment, LDPC decode 4128may output a transport block comprising data.

In at least one embodiment, CRC check 4130 may determine errors andperform one or more actions based on parity bits attached to a receivedtransport block. In at least one embodiment, CRC check 4130 may analyzeand process parity bits attached to a received transport block, orotherwise any information associated with a CRC. In at least oneembodiment, CRC check 4130 may transmit a processed transport block to aMAC layer for further processing.

It should be noted that, in various embodiments, transmitting andreceiving data, which may be a transport block or other variationthereof, may include various processes not depicted in FIG. 41. In atleast one embodiment, processes depicted in FIG. 41 are not intended tobe exhaustive and further processes such as additional modulation,mapping, multiplexing, precoding, constellation mapping/demapping, MIMOdetection, detection, decoding and variations thereof may be utilized intransmitting and receiving data as part of a network.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 41 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one of descramble 4124, rate unmatch 4126, anddemodulation/demapping 4122 is used to cause information received from aplurality of 5G new radio antennas to be decoded in parallel by aplurality of processor pipelines. In at least one embodiment, at leastone of descramble 4124, rate unmatch 4126, and demodulation/demapping4122 is used to layer demap, descramble, and de-rate-match 5G NR PUSCHdata that has been soft demapped in preparation for LDPC decoding, whereat least one thread block is used to perform operations on code blockdata elements (e.g., LLRs) in parallel.

FIG. 42 illustrates an architecture of a system 4200 of a network inaccordance with some embodiments. In at least one embodiment, system4200 is shown to include a UE 4202, a 5G access node or RAN node (shownas (R)AN node 4208), a User Plane Function (shown as UPF 4204), a DataNetwork (DN 4206), which may be, for example, operator services,Internet access or 3rd party services, and a 5G Core Network (5GC)(shown as CN 4210).

In at least one embodiment, CN 4210 includes an Authentication ServerFunction (AUSF 4214); a Core Access and Mobility Management Function(AMF 4212); a Session Management Function (SMF 4218); a Network ExposureFunction (NEF 4216); a Policy Control Function (PCF 4222); a NetworkFunction (NF) Repository Function (NRF 4220); a Unified Data Management(UDM 4224); and an Application Function (AF 4226). In at least oneembodiment, CN 4210 may also include other elements that are not shown,such as a Structured Data Storage network function (SDSF), anUnstructured Data Storage network function (UDSF), and variationsthereof.

In at least one embodiment, UPF 4204 may act as an anchor point forintra-RAT and inter-RAT mobility, an external PDU session point ofinterconnect to DN 4206, and a branching point to support multi-homedPDU session. In at least one embodiment, UPF 4204 may also performpacket routing and forwarding, packet inspection, enforce user planepart of policy rules, lawfully intercept packets (UP collection);traffic usage reporting, perform QoS handling for user plane (e.g.packet filtering, gating, UL/DL rate enforcement), perform UplinkTraffic verification (e.g., SDF to QoS flow mapping), transport levelpacket marking in uplink and downlink, and downlink packet buffering anddownlink data notification triggering. In at least one embodiment, UPF4204 may include an uplink classifier to support routing traffic flowsto a data network. In at least one embodiment, DN 4206 may representvarious network operator services, Internet access, or third partyservices.

In at least one embodiment, AUSF 4214 may store data for authenticationof UE 4202 and handle authentication related functionality. In at leastone embodiment, AUSF 4214 may facilitate a common authenticationframework for various access types.

In at least one embodiment, AMF 4212 may be responsible for registrationmanagement (e.g., for registering UE 4202, etc.), connection management,reachability management, mobility management, and lawful interception ofAMF-related events, and access authentication and authorization. In atleast one embodiment, AMF 4212 may provide transport for SM messages forSMF 4218, and act as a transparent proxy for routing SM messages. In atleast one embodiment, AMF 4212 may also provide transport for shortmessage service (SMS) messages between UE 4202 and an SMS function(SMSF) (not shown by FIG. 42). In at least one embodiment, AMF 4212 mayact as Security Anchor Function (SEA), which may include interactionwith AUSF 4214 and UE 4202 and receipt of an intermediate key that wasestablished as a result of UE 4202 authentication process. In at leastone embodiment, where USIM based authentication is used, AMF 4212 mayretrieve security material from AUSF 4214. In at least one embodiment,AMF 4212 may also include a Security Context Management (SCM) function,which receives a key from SEA that it uses to derive access-networkspecific keys. In at least one embodiment, furthermore, AMF 4212 may bea termination point of RAN CP interface (N2 reference point), atermination point of NAS (NI) signaling, and perform NAS ciphering andintegrity protection.

In at least one embodiment, AMF 4212 may also support NAS signaling witha UE 4202 over an N3 interworking-function (IWF) interface. In at leastone embodiment, N3IWF may be used to provide access to untrustedentities. In at least one embodiment, N3IWF may be a termination pointfor N2 and N3 interfaces for control plane and user plane, respectively,and as such, may handle N2 signaling from SMF and AMF for PDU sessionsand QoS, encapsulate/de-encapsulate packets for IPSec and N3 tunneling,mark N3 user-plane packets in uplink, and enforce QoS corresponding toN3 packet marking taking into account QoS requirements associated tosuch marking received over N2. In at least one embodiment, N3IWF mayalso relay uplink and downlink control-plane NAS (NI) signaling betweenUE 4202 and AMF 4212, and relay uplink and downlink user-plane packetsbetween UE 4202 and UPF 4204. In at least one embodiment, N3IWF alsoprovides mechanisms for IPsec tunnel establishment with UE 4202.

In at least one embodiment, SMF 4218 may be responsible for sessionmanagement (e.g., session establishment, modify and release, includingtunnel maintain between UPF and AN node); UE IP address allocation &management (including optional Authorization); Selection and control ofUP function; Configures traffic steering at UPF to route traffic toproper destination; termination of interfaces towards Policy controlfunctions; control part of policy enforcement and QoS; lawful intercept(for SM events and interface to LI System); termination of SM parts ofNAS messages; downlink Data Notification; initiator of AN specific SMinformation, sent via AMF over N2 to AN; determine SSC mode of asession. In at least one embodiment, SMF 4218 may include followingroaming functionality: handle local enforcement to apply QoS SLAB(VPLMN); charging data collection and charging interface (VPLMN); lawfulintercept (in VPLMN for SM events and interface to LI System); supportfor interaction with external DN for transport of signaling for PDUsession authorization/authentication by external DN.

In at least one embodiment, NEF 4216 may provide means for securelyexposing services and capabilities provided by 3GPP network functionsfor third party, internal exposure/re-exposure, Application Functions(e.g., AF 4226), edge computing or fog computing systems, etc. In atleast one embodiment, NEF 4216 may authenticate, authorize, and/orthrottle AFs. In at least one embodiment, NEF 4216 may also translateinformation exchanged with AF 4226 and information exchanged withinternal network functions. In at least one embodiment, NEF 4216 maytranslate between an AF-Service-Identifier and an internal 5GCinformation. In at least one embodiment, NEF 4216 may also receiveinformation from other network functions (NFs) based on exposedcapabilities of other network functions. In at least one embodiment,this information may be stored at NEF 4216 as structured data, or at adata storage NF using a standardized interfaces. In at least oneembodiment, stored information can then be re-exposed by NEF 4216 toother NFs and AFs, and/or used for other purposes such as analytics.

In at least one embodiment, NRF 4220 may support service discoveryfunctions, receive NF Discovery Requests from NF instances, and provideinformation of discovered NF instances to NF instances. In at least oneembodiment, NRF 4220 also maintains information of available NFinstances and their supported services.

In at least one embodiment, PCF 4222 may provide policy rules to controlplane function(s) to enforce them, and may also support unified policyframework to govern network behavior. In at least one embodiment, PCF4222 may also implement a front end (FE) to access subscriptioninformation relevant for policy decisions in a UDR of UDM 4224.

In at least one embodiment, UDM 4224 may handle subscription-relatedinformation to support a network entities' handling of communicationsessions, and may store subscription data of UE 4202. In at least oneembodiment, UDM 4224 may include two parts, an application FE and a UserData Repository (UDR). In at least one embodiment, UDM may include a UDMFE, which is in charge of processing of credentials, locationmanagement, subscription management and so on. In at least oneembodiment, several different front ends may serve a same user indifferent transactions. In at least one embodiment, UDM-FE accessessubscription information stored in an UDR and performs authenticationcredential processing; user identification handling; accessauthorization; registration/mobility manage-ment; and subscriptionmanagement. In at least one embodiment, UDR may interact with PCF 4222.In at least one embodiment, UDM 4224 may also support SMS management,wherein an SMS-FE implements a similar application logic as discussedpreviously.

In at least one embodiment, AF 4226 may provide application influence ontraffic routing, access to a Network Capability Exposure (NCE), andinteract with a policy framework for policy control. In at least oneembodiment, NCE may be a mechanism that allows a 5GC and AF 4226 toprovide information to each other via NEF 4216, which may be used foredge computing implementations. In at least one embodiment, networkoperator and third party services may be hosted close to UE 4202 accesspoint of attachment to achieve an efficient service delivery through areduced end-to-end latency and load on a transport network. In at leastone embodiment, for edge computing implementations, 5GC may select a UPF4204 close to UE 4202 and execute traffic steering from UPF 4204 to DN4206 via N6 interface. In at least one embodiment, this may be based onUE subscription data, UE location, and information provided by AF 4226.In at least one embodiment, AF 4226 may influence UPF (re)selection andtraffic routing. In at least one embodiment, based on operatordeployment, when AF 4226 is considered to be a trusted entity, a networkoperator may permit AF 4226 to interact directly with relevant NFs.

In at least one embodiment, CN 4210 may include an SMSF, which may beresponsible for SMS subscription checking and verification, and relayingSM messages to/from UE 4202 to/from other entities, such as anSMS-GMSC/IWMSC/SMS-router. In at least one embodiment, SMS may alsointeract with AMF 4212 and UDM 4224 for notification procedure that UE4202 is available for SMS transfer (e.g., set a UE not reachable flag,and notifying UDM 4224 when UE 4202 is available for SMS).

In at least one embodiment, system 4200 may include followingservice-based interfaces: Namf: Service-based interface exhibited byAMF; Nsmf: Service-based interface exhibited by SMF; Nnef: Service-basedinterface exhibited by NEF; Npcf: Service-based interface exhibited byPCF; Nudm: Service-based interface exhibited by UDM; Naf: Service-basedinterface exhibited by AF; Nnrf: Service-based interface exhibited byNRF; and Nausf: Service-based interface exhibited by AUSF.

In at least one embodiment, system 4200 may include following referencepoints: N1: Reference point between UE and AMF; N2: Reference pointbetween (R)AN and AMF; N3: Reference point between (R)AN and UPF; N4:Reference point between SMF and UPF; and N6: Reference point between UPFand a Data Network. In at least one embodiment, there may be many morereference points and/or service-based interfaces between a NF servicesin NFs, how-ever, these interfaces and reference points have beenomitted for clarity. In at least one embodiment, an NS reference pointmay be between a PCF and AF; an N7 reference point may be between PCFand SMF; an N11 reference point between AMF and SMF; etc. In at leastone embodiment, CN 4210 may include an Nx interface, which is aninter-CN interface between MME and AMF 4212 in order to enableinterworking between CN 4210 and CN 7242.

In at least one embodiment, system 4200 may include multiple RAN nodes(such as (R)AN node 4208) wherein an Xn interface is defined between twoor more (R)AN node 4208 (e.g., gNBs) that connecting to 5GC 410, betweena (R)AN node 4208 (e.g., gNB) connecting to CN 4210 and an eNB (e.g., amacro RAN node), and/or between two eNBs connecting to CN 4210.

In at least one embodiment, Xn interface may include an Xn user plane(Xn-U) interface and an Xn control plane (Xn-C) interface. In at leastone embodiment, Xn-U may provide non-guaranteed delivery of user planePDUs and support/provide data forwarding and flow control functionality.In at least one embodiment, Xn-C may provide management and errorhandling functionality, functionality to manage a Xn-C interface;mobility support for UE 4202 in a connected mode (e.g., CM-CONNECTED)including functionality to manage UE mobility for connected mode betweenone or more (R)AN node 4208. In at least one embodiment, mobilitysupport may include context transfer from an old (source) serving (R)ANnode 4208 to new (target) serving (R)AN node 4208; and control of userplane tunnels between old (source) serving (R)AN node 4208 to new(target) serving (R)AN node 4208.

In at least one embodiment, a protocol stack of a Xn-U may include atransport network layer built on Internet Protocol (IP) transport layer,and a GTP-U layer on top of a UDP and/or IP layer(s) to carry user planePDUs. In at least one embodiment, Xn-C protocol stack may include anapplication layer signaling protocol (referred to as Xn ApplicationProtocol (Xn-AP)) and a transport network layer that is built on an SCTPlayer. In at least one embodiment, SCTP layer may be on top of an IPlayer. In at least one embodiment, SCTP layer provides a guaranteeddelivery of application layer messages. In at least one embodiment, in atransport IP layer point-to-point transmission is used to deliversignaling PDUs. In at least one embodiment, Xn-U protocol stack and/or aXn-C protocol stack may be same or similar to an user plane and/orcontrol plane protocol stack(s) shown and described herein.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 42 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one component of system 4200, such as RAN node4208, is used to cause information received from a plurality of 5G newradio antennas to be decoded in parallel by a plurality of processorpipelines. In at least one embodiment, at least one component of system4200, such as RAN node 4208, is used to layer demap, descramble, andde-rate-match 5G NR PUSCH data that has been soft demapped inpreparation for LDPC decoding, where at least one thread block is usedto perform operations on code block data elements (e.g., LLRs) inparallel.

FIG. 43 is an illustration of a control plane protocol stack inaccordance with some embodiments. In at least one embodiment, a controlplane 4300 is shown as a communications protocol stack between UE 3802(or alternatively, UE 3804), RAN 3816, and MME(s) 3828.

In at least one embodiment, PHY layer 4302 may transmit or receiveinformation used by MAC layer 4304 over one or more air interfaces. Inat least one embodiment, PHY layer 4302 may further perform linkadaptation or adaptive modulation and coding (AMC), power control, cellsearch (e.g., for initial synchronization and handover purposes), andother measurements used by higher layers, such as an RRC layer 4310. Inat least one embodiment, PHY layer 4302 may still further perform errordetection on transport channels, forward error correction (FEC)coding/de-coding of transport channels, modulation/demodulation ofphysical channels, interleaving, rate matching, mapping onto physicalchannels, and Multiple Input Multiple Output (MIMO) antenna processing.

In at least one embodiment, MAC layer 4304 may perform mapping betweenlogical channels and transport channels, multiplexing of MAC servicedata units (SDUs) from one or more logical channels onto transportblocks (TB) to be delivered to PHY via transport channels,de-multiplexing MAC SDUs to one or more logical channels from transportblocks (TB) delivered from PHY via transport channels, multiplexing MACSDUs onto TBs, scheduling information reporting, error correctionthrough hybrid automatic repeat request (HARD), and logical channelprioritization.

In at least one embodiment, RLC layer 4306 may operate in a plurality ofmodes of operation, including: Transparent Mode™, Unacknowledged Mode(UM), and Acknowledged Mode (AM). In at least one embodiment, RLC layer4306 may execute transfer of upper layer protocol data units (PDUs),error correction through automatic repeat request (ARQ) for AM datatransfers, and concatenation, segmentation and reassembly of RLC SDUsfor UM and AM data transfers. In at least one embodiment, RLC layer 4306may also execute re-segmentation of RLC data PDUs for AM data transfers,reorder RLC data PDUs for UM and AM data transfers, detect duplicatedata for UM and AM data transfers, discard RLC SDUs for UM and AM datatransfers, detect protocol errors for AM data transfers, and perform RLCre-establishment.

In at least one embodiment, PDCP layer 4308 may execute headercompression and decompression of IP data, maintain PDCP Sequence Numbers(SNs), perform in-sequence delivery of upper layer PDUs atre-establishment of lower layers, eliminate duplicates of lower layerSDUs at re-establishment of lower layers for radio bearers mapped on RLCAM, cipher and decipher control plane data, perform integrity protectionand integrity verification of control plane data, control timer-baseddiscard of data, and perform security operations (e.g., ciphering,deciphering, integrity protection, integrity verification, etc.).

In at least one embodiment, main services and functions of a RRC layer4310 may include broadcast of system information (e.g., included inMaster Information Blocks (MIBs) or System Information Blocks (SIBs)related to a non-access stratum (NAS)), broadcast of system informationrelated to an access stratum (AS), paging, establishment, maintenanceand release of an RRC connection between an UE and E-UTRAN (e.g., RRCconnection paging, RRC connection establishment, RRC connectionmodification, and RRC connection release), establishment, configuration,maintenance and release of point-to-point radio bearers, securityfunctions including key management, inter radio access technology (RAT)mobility, and measurement configuration for UE measurement reporting. Inat least one embodiment, said MIBs and SIBs may comprise one or moreinformation elements (IEs), which may each comprise individual datafields or data structures.

In at least one embodiment, UE 3802 and RAN 3816 may utilize a Uuinterface (e.g., an LTE-Uu interface) to exchange control plane data viaa protocol stack comprising PHY layer 4302, MAC layer 4304, RLC layer4306, PDCP layer 4308, and RRC layer 4310.

In at least one embodiment, non-access stratum (NAS) protocols (NASprotocols 4312) form a highest stratum of a control plane between UE3802 and MME(s) 3828. In at least one embodiment, NAS protocols 4312support mobility of UE 3802 and session management procedures toestablish and maintain IP connectivity between UE 3802 and P-GW 3834.

In at least one embodiment, Si Application Protocol (S1-AP) layer (Si-APlayer 4322) may support functions of a Si interface and compriseElementary Procedures (EPs). In at least one embodiment, an EP is a unitof interaction between RAN 3816 and CN 3828. In at least one embodiment,S1-AP layer services may comprise two groups: UE-associated services andnon UE-associated services. In at least one embodiment, these servicesperform functions including, but not limited to: E-UTRAN Radio AccessBearer (E-RAB) management, UE capability indication, mobility, NASsignaling transport, RAN Information Management (RIM), and configurationtransfer.

In at least one embodiment, Stream Control Transmission Protocol (SCTP)layer (alternatively referred to as a stream control transmissionprotocol/internet protocol (SCTP/IP) layer) (SCTP layer 4320) may ensurereliable delivery of signaling messages between RAN 3816 and MME(s) 3828based, in part, on an IP protocol, supported by an IP layer 4318. In atleast one embodiment, L2 layer 4316 and an L1 layer 4314 may refer tocommunication links (e.g., wired or wireless) used by a RAN node and MMEto exchange information.

In at least one embodiment, RAN 3816 and MME(s) 3828 may utilize anS1-MME interface to exchange control plane data via a protocol stackcomprising a L1 layer 4314, L2 layer 4316, IP layer 4318, SCTP layer4320, and Si-AP layer 4322.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 43 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one component of RAN 4316, such as PHY 4302, isused to cause information received from a plurality of 5G new radioantennas to be decoded in parallel by a plurality of processorpipelines. In at least one embodiment, at least one component of RAN4316, such as PHY 4302, is used to layer demap, descramble, andde-rate-match 5G NR PUSCH data that has been soft demapped inpreparation for LDPC decoding, where at least one thread block is usedto perform operations on code block data elements (e.g., LLRs) inparallel.

FIG. 44 is an illustration of a user plane protocol stack in accordancewith at least one embodiment. In at least one embodiment, a user plane4400 is shown as a communications protocol stack between a UE 3802, RAN3816, S-GW 3830, and P-GW 3834. In at least one embodiment, user plane4400 may utilize a same protocol layers as control plane 4300. In atleast one embodiment, for example, UE 3802 and RAN 3816 may utilize a Uuinterface (e.g., an LTE-Uu interface) to exchange user plane data via aprotocol stack comprising PHY layer 4302, MAC layer 4304, RLC layer4306, PDCP layer 4308.

In at least one embodiment, General Packet Radio Service (GPRS)Tunneling Protocol for a user plane (GTP-U) layer (GTP-U layer 4404) maybe used for carrying user data within a GPRS core network and between aradio access network and a core network. In at least one embodiment,user data transported can be packets in any of IPv4, IPv6, or PPPformats, for example. In at least one embodiment, UDP and IP security(UDP/IP) layer (UDP/IP layer 4402) may provide checksums for dataintegrity, port numbers for addressing different functions at a sourceand destination, and encryption and authentication on selected dataflows. In at least one embodiment, RAN 3816 and S-GW 3830 may utilize anS1-U interface to exchange user plane data via a protocol stackcomprising L1 layer 4314, L2 layer 4316, UDP/IP layer 4402, and GTP-Ulayer 4404. In at least one embodiment, S-GW 3830 and P-GW 3834 mayutilize an S5/S8a interface to exchange user plane data via a protocolstack comprising L1 layer 4314, L2 layer 4316, UDP/IP layer 4402, andGTP-U layer 4404. In at least one embodiment, as discussed above withrespect to FIG. 43, NAS protocols support a mobility of UE 3802 andsession management procedures to establish and maintain IP connectivitybetween UE 3802 and P-GW 3834.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 44 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one component of RAN 4416, such as PHY 4402, isused to cause information received from a plurality of 5G new radioantennas to be decoded in parallel by a plurality of processorpipelines. In at least one embodiment, at least one component of RAN4416, such as PHY 4402, is used to layer demap, descramble, andde-rate-match 5G NR PUSCH data that has been soft demapped inpreparation for LDPC decoding, where at least one thread block is usedto perform operations on code block data elements (e.g., LLRs) inparallel.

FIG. 45 illustrates components 4500 of a core network in accordance withat least one embodiment. In at least one embodiment, components of CN3838 may be implemented in one physical node or separate physical nodesincluding components to read and execute instructions from amachine-readable or computer-readable medium (e.g., a non-transitorymachine-readable storage medium). In at least one embodiment, NetworkFunctions Virtualization (NFV) is utilized to virtualize any or all ofabove described network node functions via executable instructionsstored in one or more computer readable storage mediums (described infurther detail below). In at least one embodiment, a logicalinstantiation of CN 3838 may be referred to as a network slice 4502(e.g., network slice 4502 is shown to include HSS 3832, MME(s) 3828, andS-GW 3830). In at least one embodiment, a logical instantiation of aportion of CN 3838 may be referred to as a network sub-slice 4504 (e.g.,network sub-slice 4504 is shown to include P-GW 3834 and PCRF 3836).

In at least one embodiment, NFV architectures and infrastructures may beused to virtualize one or more network functions, alternativelyperformed by proprietary hardware, onto physical resources comprising acombination of industry-standard server hardware, storage hardware, orswitches. In at least one embodiment, NFV systems can be used to executevirtual or reconfigurable implementations of one or more EPCcomponents/functions.

In at least one embodiment, at least one component shown or describedwith respect to FIG. 45 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one component of components 4500 is used to causeinformation received from a plurality of 5G new radio antennas to bedecoded in parallel by a plurality of processor pipelines. In at leastone embodiment, at least one component of components 4500 is used tolayer demap, descramble, and de-rate-match 5G NR PUSCH data that hasbeen soft demapped in preparation for LDPC decoding, where at least onethread block is used to perform operations on code block data elements(e.g., LLRs) in parallel.

FIG. 46 is a block diagram illustrating components, according to atleast one embodiment, of a system 4600 to support network functionvirtualization (NFV). In at least one embodiment, system 4600 isillustrated as including a virtualized infrastructure manager (shown asVIM 4602), a network function virtualization infrastructure (shown asNFVI 4604), a VNF manager (shown as VNFM 4606), virtualized networkfunctions (shown as VNF 4608), an element manager (shown as EM 4610), anNFV Orchestrator (shown as NFVO 4612), and a network manager (shown asNM 4614).

In at least one embodiment, VIM 4602 manages resources of NFVI 4604. Inat least one embodiment, NFVI 4604 can include physical or virtualresources and applications (including hypervisors) used to executesystem 4600. In at least one embodiment, VIM 4602 may manage a lifecycle of virtual resources with NFVI 4604 (e.g., creation, maintenance,and tear down of virtual machines (VMs) associated with one or morephysical resources), track VM instances, track performance, fault andsecurity of VM instances and associated physical resources, and exposeVM instances and associated physical resources to other managementsystems.

In at least one embodiment, VNFM 4606 may manage VNF 4608. In at leastone embodiment, VNF 4608 may be used to execute EPCcomponents/functions. In at least one embodiment, VNFM 4606 may manage alife cycle of VNF 4608 and track performance, fault and security ofvirtual aspects of VNF 4608. In at least one embodiment, EM 4610 maytrack performance, fault and security of functional aspects of VNF 4608.In at least one embodiment, tracking data from VNFM 4606 and EM 4610 maycomprise, for example, performance measurement (PM) data used by VIM4602 or NFVI 4604. In at least one embodiment, both VNFM 4606 and EM4610 can scale up/down a quantity of VNFs of system 4600.

In at least one embodiment, NFVO 4612 may coordinate, authorize, releaseand engage resources of NFVI 4604 in order to provide a requestedservice (e.g., to execute an EPC function, component, or slice). In atleast one embodiment, NM 4614 may provide a package of end-userfunctions with responsibility for a management of a network, which mayinclude network elements with VNFs, non-virtualized network functions,or both (management of VNFs may occur via EM 4610).

In at least one embodiment, at least one component shown or describedwith respect to FIG. 46 is utilized to implement techniques and/orfunctions described in connection with FIGS. 1-6. In at least oneembodiment, at least one component of system 4600, such as at least oneVNF 4608, is used to cause information received from a plurality of 5Gnew radio antennas to be decoded in parallel by a plurality of processorpipelines. In at least one embodiment, at least one component of system4600, such as at least one VNF 4608, is used to layer demap, descramble,and de-rate-match 5G NR PUSCH data that has been soft demapped inpreparation for LDPC decoding, where at least one thread block is usedto perform operations on code block data elements (e.g., LLRs) inparallel.

At least one embodiment can be described in view of at least one offollowing clauses:

1. A processor, comprising:

one or more circuits to cause information received from a plurality offifth-generation (5G) new radio antennas to be decoded in parallel by acorresponding plurality of processor pipelines.

2. The processor of clause 1, wherein the information received from theplurality of 5G new radio antennas has been processed by soft demappingand wherein decoding the information comprises layer demapping,descrambling, and de-rate-matching.

3. The processor of any one of clauses 1-2, wherein decoding theinformation received from the plurality of 5G new radio antennascomprises assigning the information to multiple groups of threads,wherein each group of threads of the multiple group of threads is tocompute, from a transfer block, a corresponding code block to bedecoded.

4. The processor of any one of clauses 1-3, wherein the informationincludes data in a first mapping configuration, and wherein decoding theinformation comprises layer demapping and descrambling the data, andstoring the layer demapped and descrambled data in a second mappingconfiguration.

5. The processor of any one of clauses 1-4, wherein the one or morecircuits cause the plurality of processor pipelines to combine the layerdemapped and descrambled data with previously received data in a hybridautomatic repeat request (HARQ) buffer.

6. The processor of any one of clauses 1-5, wherein the informationreceived from the plurality of 5G new radio antennas has been processedby soft demapping and stored as values that represent log-likelihoodratios, and the one or more circuits cause the plurality of processorpipelines to layer demap the values according to a mapping function.

7. The processor of any one of clauses 1-6, wherein the informationreceived from the plurality of 5G new radio antennas has been processedby soft demapping, and wherein decoding the information comprisesde-interleaving the information in parallel.

8. The processor of any one of clauses 2-7, wherein de-rate-matchingincludes inserting filler bits.

9. A machine-readable medium having stored thereon a set ofinstructions, which if performed, cause a parallel processor to atleast:

cause information transmitted using a plurality of fifth-generation (5G)new radio signals to be decoded by the parallel processor by schedulinga plurality of thread groups each corresponding to at least one of theplurality of 5G new radio signals on the parallel processor.

10. The machine-readable medium of clause 9, wherein the informationincludes data that has been processed by soft demapping and whereinscheduling the plurality of thread groups includes scheduling theplurality of thread groups to layer demap, descramble, and de-rate-matchthe data.

11. The machine-readable medium of any one of clauses 9-10 wherein theinformation includes data that has been processed by soft demapping andwherein scheduling the plurality of thread groups includes schedulingthe plurality of thread groups to read values from the data thatcorrespond to log-likelihood ratios, and descramble the values bymultiplying the values according to a descrambling function.

12. The machine-readable medium of any one of clauses 9-11, whereinscheduling the plurality of thread groups includes scheduling theplurality of thread groups to combine the descrambled values withpreviously received data in a hybrid automatic repeat request (HARQ)buffer.

13. The machine-readable medium of any one of clauses 9-12, whereinscheduling the plurality of thread groups includes scheduling theplurality of thread groups to de-interleave code block data elements.

14. The machine-readable medium of any one of clauses 9-13, whereinscheduling the plurality of thread groups includes scheduling theplurality of thread groups to de-rate-match code blocks, at least inpart by inserting filler bits.

15. The machine-readable medium of any one of clauses 9-14, whereinscheduling the plurality of thread groups to layer demap the data isbased at least in part on a mapping function that uses a thread blockindex as a parameter.

16. The machine-readable medium of any one of clauses 9-15, wherein themapping function also uses a transport block size and a number ofmultiple-input multiple-output (MIMO) layers as parameters.

17. A system, comprising:

one or more processors to cause information received from a plurality offifth-generation (5G) new radio antennas to be decoded in parallel by acorresponding plurality of processor pipelines; and

one or more memories to store data corresponding to the information.

18. The system of clause 17, wherein the information received from theplurality of 5G new radio antennas has been processed by soft demappingand wherein decoding the information comprises layer demapping,descrambling, and de-rate-matching.

19. The system of any one of clauses 17-18, wherein decoding theinformation received from the plurality of 5G new radio antennascomprises assigning the information to multiple groups of threads,wherein each group of threads of the multiple groups of threads is tocompute, from a transfer block, a corresponding code block to bedecoded.

20. The system of any one of clauses 17-19, wherein the one or moreprocessors are to cause the information to be decoded in parallel, atleast in part, by causing the data to be transformed for decoding by adecoder and storing the transformed data in the one or more memories,wherein causing the data to be transformed includes causing the data tobe de-rate-matched in parallel by the plurality of processor pipelines.

21. The system of any one of clauses 17-20, wherein storing thetransformed data in the one or more memories includes combining thetransformed data with contents of a hybrid automatic repeat request(HARQ) buffer.

22. The system of any one of clauses 17-21, wherein the one or moreprocessors are to cause the information to be decoded in parallel, atleast in part, by causing transport blocks to be extracted from the dataand causing code blocks to be extracted from the transport blocks.

23. The system of any one of clauses 17-22, wherein the data includesvalues that correspond to log-likelihood ratios, the one or moreprocessors are to cause the information to be decoded in parallel, atleast in part, by descrambling the data based, at least in part, onmultiplying the values according to a descrambling function.

24. The system of any one of clauses 17-23, wherein the data is storedin the one or more memories in a first mapping configuration, and theone or more processors cause the plurality of processor pipelines to:

read the data from the one or more memories in parallel;

layer demap the data in parallel;

de-rate-match the data in parallel;

descramble the data in parallel; and

combine the layer demapped, de-rate-matched, descrambled data withcontents of a hybrid automatic repeat request (HARQ) buffer in a secondmapping configuration.

25. A method, comprising:

accessing, with a plurality of thread groups running on a parallelprocessor, data that corresponds to information transmitted using aplurality of fifth-generation (5G) new radio signals; and

causing, with the plurality of thread groups, the data to be decoded.

26. The method of clause 25, wherein the data has been processed by softdemapping and wherein causing the data to be decoded includestransforming the data for decoding by a decoder.

27. The method of any one of clauses 25-26, wherein the data includesdata elements of a plurality of code blocks, and transforming the dataincludes assigning one or more thread groups of the plurality of threadgroups to handle each code block.

28. The method of any one of clauses 25-27, wherein transforming thedata includes layer demapping the data.

29. The method of any one of clauses 25-28, wherein transforming thedata includes de-rate-matching the data.

30. The method of any one of clauses 25-29, wherein transforming thedata includes reading values from the data that correspond tolog-likelihood ratios, and descrambling the values by multiplying thevalues according to a descrambling function.

31. The method of any one of clauses 25-30, further comprising:

combining the descrambled values with previously received data in ahybrid automatic repeat request (HARD) buffer.

32. The method of any one of clauses 25-31, wherein transforming thedata includes transforming the data for decoding by a low density paritycheck (LDPC) decoder in a fifth-generation (5G) new radio (NR) physicaluplink shared channel (PUSCH) physical layer (PHY).

Other variations are within spirit of present disclosure. Thus, whiledisclosed techniques are susceptible to various modifications andalternative constructions, certain illustrated embodiments thereof areshown in drawings and have been described above in detail. It should beunderstood, however, that there is no intention to limit disclosure tospecific form or forms disclosed, but on contrary, intention is to coverall modifications, alternative constructions, and equivalents fallingwithin spirit and scope of disclosure, as defined in appended claims.

Use of terms “a” and “an” and “the” and similar referents in context ofdescribing disclosed embodiments (especially in context of followingclaims) are to be construed to cover both singular and plural, unlessotherwise indicated herein or clearly contradicted by context, and notas a definition of a term. Terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (meaning“including, but not limited to,”) unless otherwise noted. term“connected,” when unmodified and referring to physical connections, isto be construed as partly or wholly contained within, attached to, orjoined together, even if there is something intervening. Recitation ofranges of values herein are merely intended to serve as a shorthandmethod of referring individually to each separate value falling withinrange, unless otherwise indicated herein and each separate value isincorporated into specification as if it were individually recitedherein. use of term “set” (e.g., “a set of items”) or “subset” unlessotherwise noted or contradicted by context, is to be construed as anonempty collection comprising one or more members. Further, unlessotherwise noted or contradicted by context, term “subset” of acorresponding set does not necessarily denote a proper subset ofcorresponding set, but subset and corresponding set may be equal.

Conjunctive language, such as phrases of form “at least one of A, B, andC,” or “at least one of A, B and C,” unless specifically statedotherwise or otherwise clearly contradicted by context, is otherwiseunderstood with context as used in general to present that an item,term, etc., may be either A or B or C, or any nonempty subset of set ofA and B and C. For instance, in illustrative example of a set havingthree members, conjunctive phrases “at least one of A, B, and C” and “atleast one of A, B and C” refer to any of following sets: {A}, {B}, {C},{A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctive language isnot generally intended to imply that certain embodiments require atleast one of A, at least one of B and at least one of C each to bepresent. In addition, unless otherwise noted or contradicted by context,term “plurality” indicates a state of being plural (e.g., “a pluralityof items” indicates multiple items). number of items in a plurality isat least two, but can be more when so indicated either explicitly or bycontext. Further, unless stated otherwise or otherwise clear fromcontext, phrase “based on” means “based at least in part on” and not“based solely on.”

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. In at least one embodiment, a process such asthose processes described herein (or variations and/or combinationsthereof) is performed under control of one or more computer systemsconfigured with executable instructions and is implemented as code(e.g., executable instructions, one or more computer programs or one ormore applications) executing collectively on one or more processors, byhardware or combinations thereof. In at least one embodiment, code isstored on a computer-readable storage medium, for example, in form of acomputer program comprising a plurality of instructions executable byone or more processors. In at least one embodiment, a computer-readablestorage medium is a non-transitory computer-readable storage medium thatexcludes transitory signals (e.g., a propagating transient electric orelectromagnetic transmission) but includes non-transitory data storagecircuitry (e.g., buffers, cache, and queues) within transceivers oftransitory signals. In at least one embodiment, code (e.g., executablecode or source code) is stored on a set of one or more non-transitorycomputer-readable storage media having stored thereon executableinstructions (or other memory to store executable instructions) that,when executed (i.e., as a result of being executed) by one or moreprocessors of a computer system, cause computer system to performoperations described herein. set of non-transitory computer-readablestorage media, in at least one embodiment, comprises multiplenon-transitory computer-readable storage media and one or more ofindividual non-transitory storage media of multiple non-transitorycomputer-readable storage media lack all of code while multiplenon-transitory computer-readable storage media collectively store all ofcode. In at least one embodiment, executable instructions are executedsuch that different instructions are executed by differentprocessors—for example, a non-transitory computer-readable storagemedium store instructions and a main central processing unit (“CPU”)executes some of instructions while a graphics processing unit (“GPU”)executes other instructions. In at least one embodiment, differentcomponents of a computer system have separate processors and differentprocessors execute different subsets of instructions.

Accordingly, in at least one embodiment, computer systems are configuredto implement one or more services that singly or collectively performoperations of processes described herein and such computer systems areconfigured with applicable hardware and/or software that enableperformance of operations. Further, a computer system that implements atleast one embodiment of present disclosure is a single device and, inanother embodiment, is a distributed computer system comprising multipledevices that operate differently such that distributed computer systemperforms operations described herein and such that a single device doesnot perform all operations.

Use of any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate embodiments ofdisclosure and does not pose a limitation on scope of disclosure unlessotherwise claimed. No language in specification should be construed asindicating any non-claimed element as essential to practice ofdisclosure.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

In description and claims, terms “coupled” and “connected,” along withtheir derivatives, may be used. It should be understood that these termsmay be not intended as synonyms for each other. Rather, in particularexamples, “connected” or “coupled” may be used to indicate that two ormore elements are in direct or indirect physical or electrical contactwith each other. “Coupled” may also mean that two or more elements arenot in direct contact with each other, but yet still co-operate orinteract with each other.

Unless specifically stated otherwise, it may be appreciated thatthroughout specification terms such as “processing,” “computing,”“calculating,” “determining,” or like, refer to action and/or processesof a computer or computing system, or similar electronic computingdevice, that manipulate and/or transform data represented as physical,such as electronic, quantities within computing system's registersand/or memories into other data similarly represented as physicalquantities within computing system's memories, registers or other suchinformation storage, transmission or display devices.

In a similar manner, term “processor” may refer to any device or portionof a device that processes electronic data from registers and/or memoryand transform that electronic data into other electronic data that maybe stored in registers and/or memory. As non-limiting examples,“processor” may be a CPU or a GPU. A “computing platform” may compriseone or more processors. As used herein, “software” processes mayinclude, for example, software and/or hardware entities that performwork over time, such as tasks, threads, and intelligent agents. Also,each process may refer to multiple processes, for carrying outinstructions in sequence or in parallel, continuously or intermittently.terms “system” and “method” are used herein interchangeably insofar assystem may embody one or more methods and methods may be considered asystem.

In present document, references may be made to obtaining, acquiring,receiving, or inputting analog or digital data into a subsystem,computer system, or computer-implemented machine. process of obtaining,acquiring, receiving, or inputting analog and digital data can beaccomplished in a variety of ways such as by receiving data as aparameter of a function call or a call to an application programminginterface. In some implementations, process of obtaining, acquiring,receiving, or inputting analog or digital data can be accomplished bytransferring data via a serial or parallel interface. In anotherimplementation, process of obtaining, acquiring, receiving, or inputtinganalog or digital data can be accomplished by transferring data via acomputer network from providing entity to acquiring entity. Referencesmay also be made to providing, outputting, transmitting, sending, orpresenting analog or digital data. In various examples, process ofproviding, outputting, transmitting, sending, or presenting analog ordigital data can be accomplished by transferring data as an input oroutput parameter of a function call, a parameter of an applicationprogramming interface or interprocess communication mechanism.

Although discussion above sets forth example implementations ofdescribed techniques, other architectures may be used to implementdescribed functionality, and are intended to be within scope of thisdisclosure. Furthermore, although specific distributions ofresponsibilities are defined above for purposes of discussion, variousfunctions and responsibilities might be distributed and divided indifferent ways, depending on circumstances.

Furthermore, although subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that subject matter claimed in appended claims is notnecessarily limited to specific features or acts described. Rather,specific features and acts are disclosed as exemplary forms ofimplementing claims.

1. A processor, comprising: one or more circuits to cause informationreceived from a plurality of antennas to be decoded in parallel by acorresponding plurality of processor pipelines, of one or more parallelprocessing units (PPUs).
 2. The processor of claim 1, wherein theinformation received from the plurality of antennas has been processedby soft demapping and wherein decoding the information comprises layerdemapping, descrambling, and de-rate-matching.
 3. The processor of claim1, wherein decoding the information received from the plurality ofantennas comprises assigning the information to multiple groups ofthreads, wherein each group of threads of the multiple group of threadsis to compute, from a transfer block, a corresponding code block to bedecoded.
 4. The processor of claim 1, wherein the information includesdata in a first mapping configuration, and wherein decoding theinformation comprises layer demapping and descrambling the data, andstoring the layer demapped and descrambled data in a second mappingconfiguration.
 5. The processor of claim 4, wherein the one or morecircuits cause the plurality of processor pipelines to combine the layerdemapped and descrambled data with previously received data in a hybridautomatic repeat request (HARQ) buffer.
 6. The processor of claim 1,wherein the information received from the plurality of antennas has beenprocessed by soft demapping and stored as values that representlog-likelihood ratios, and the one or more circuits cause the pluralityof processor pipelines to layer demap the values according to a mappingfunction.
 7. The processor of claim 1, wherein the information receivedfrom the plurality of antennas has been processed by soft demapping, andwherein decoding the information comprises de-interleaving theinformation in parallel.
 8. The processor of claim 2, whereinde-rate-matching includes inserting filler bits.
 9. A non-transitorymachine-readable medium having stored thereon a set of instructions,which if performed, cause a parallel processor to at least: causeinformation transmitted using a plurality of radio signals to be decodedby the parallel processor by scheduling a plurality of thread groupseach corresponding to at least one of the plurality of radio signals onthe parallel processor, wherein the parallel processor includes one ormore parallel processing units (PPUs).
 10. The machine-readable mediumof claim 9, wherein the information includes data that has beenprocessed by soft demapping and wherein scheduling the plurality ofthread groups includes scheduling the plurality of thread groups tolayer demap, descramble, and de-rate-match the data.
 11. Themachine-readable medium of claim 9 wherein the information includes datathat has been processed by soft demapping and wherein scheduling theplurality of thread groups includes scheduling the plurality of threadgroups to read values from the data that correspond to log-likelihoodratios, and descramble the values by multiplying the values according toa descrambling function.
 12. The machine-readable medium of claim 11,wherein scheduling the plurality of thread groups includes schedulingthe plurality of thread groups to combine the descrambled values withpreviously received data in a hybrid automatic repeat request (HARQ)buffer.
 13. The machine-readable medium of claim 9, wherein schedulingthe plurality of thread groups includes scheduling the plurality ofthread groups to de-interleave code block data elements.
 14. Themachine-readable medium of claim 9, wherein scheduling the plurality ofthread groups includes scheduling the plurality of thread groups tode-rate-match code blocks, at least in part by inserting filler bits.15. The machine-readable medium of claim 10, wherein scheduling theplurality of thread groups to layer demap the data is based at least inpart on a mapping function that uses a thread block index as aparameter.
 16. The machine-readable medium of claim 15, wherein themapping function also uses a transport block size and a number ofmultiple-input multiple-output (MIMO) layers as parameters.
 17. Asystem, comprising: one or more processors to cause information receivedfrom a plurality of antennas to be decoded in parallel by acorresponding plurality of processor pipelines of one or more parallelprocessing units (PPUs); and one or more memories to store datacorresponding to the information.
 18. The system of claim 17, whereinthe information received from the plurality of antennas has beenprocessed by soft demapping and wherein decoding the informationcomprises layer demapping, descrambling, and de-rate-matching.
 19. Thesystem of claim 17, wherein decoding the information received from theplurality of antennas comprises assigning the information to multiplegroups of threads, wherein each group of threads of the multiple groupsof threads is to compute, from a transfer block, a corresponding codeblock to be decoded.
 20. The system of claim 17, wherein the one or moreprocessors are to cause the information to be decoded in parallel, atleast in part, by causing the data to be transformed for decoding by adecoder and storing the transformed data in the one or more memories,wherein causing the data to be transformed includes causing the data tobe de-rate-matched in parallel by the plurality of processor pipelines.21. The system of claim 20, wherein storing the transformed data in theone or more memories includes combining the transformed data withcontents of a hybrid automatic repeat request (HARQ) buffer.
 22. Thesystem of claim 17, wherein the one or more processors are to cause theinformation to be decoded in parallel, at least in part, by causingtransport blocks to be extracted from the data and causing code blocksto be extracted from the transport blocks.
 23. The system of claim 17,wherein the data includes values that correspond to log-likelihoodratios, the one or more processors are to cause the information to bedecoded in parallel, at least in part, by descrambling the data based,at least in part, on multiplying the values according to a descramblingfunction.
 24. The system of claim 17, wherein the data is stored in theone or more memories in a first mapping configuration, and the one ormore processors cause the plurality of processor pipelines to: read thedata from the one or more memories in parallel; layer demap the data inparallel; de-rate-match the data in parallel; descramble the data inparallel; and combine the layer demapped, de-rate-matched, descrambleddata with contents of a hybrid automatic repeat request (HARQ) buffer ina second mapping configuration.
 25. A method, comprising: accessing,with a plurality of thread groups running on a parallel processor thatincludes one or more parallel processing units (PPUs), data thatcorresponds to information transmitted using a plurality of radiosignals; causing, with the plurality of thread groups, the data to bedecoded.
 26. The method of claim 25, wherein the data has been processedby soft demapping and wherein causing the data to be decoded includestransforming the data for decoding by a decoder.
 27. The method of claim26, wherein the data includes data elements of a plurality of codeblocks, and transforming the data includes assigning one or more threadgroups of the plurality of thread groups to handle each code block. 28.The method of claim 26, wherein transforming the data includes layerdemapping the data.
 29. The method of claim 26, wherein transforming thedata includes de-rate-matching the data.
 30. The method of claim 26,wherein transforming the data includes reading values from the data thatcorrespond to log-likelihood ratios, and descrambling the values bymultiplying the values according to a descrambling function.
 31. Themethod of claim 30, further comprising: combining the descrambled valueswith previously received data in a hybrid automatic repeat request(HARD) buffer.
 32. The method of claim 26, wherein transforming the dataincludes transforming the data for decoding by a low density paritycheck (LDPC) decoder in a physical uplink shared channel (PUSCH)physical layer (PHY).